IFN702: Educational Data Mining project
version 1.0 — Zhiying Zhou: 9835580
Introduction
This study will process, exploration, analyze and predict Student’s academic performance based on a XAPI Educational Mining Datasets.
The XAPI Educational Mining Datasets is download from the Kaggle website. It was collected from an e-Learning system called Kalboard 360 using Experience API Web service (XAPI).
The datasets consist of 480 student records and 16 features. The features are classified into three major categories: 1. Demographic features such as gender, grade levels, topic and nationality. 2. Academic background features such as educational stage, grade Level and section. 3. Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.
Attributes 1 Gender - student’s gender (nominal: ‘Male’ or ‘Female’) 2 Nationality- student’s nationality (nominal:‘Kuwait’,‘Lebanon’,‘Egypt’,‘SaudiArabia’,‘USA’,‘Jordan’,‘Venezuela’,‘Iran’,‘Tunis’,‘Morocco’,‘Syria’,‘Palestine’,‘Iraq’,‘Lybia’) 3 Place of birth- student’s Place of birth (nominal:‘Kuwait’,‘Lebanon’,‘Egypt’,‘SaudiArabia’,‘USA’,‘Jordan’,‘Venezuela’,‘Iran’,‘Tunis’,‘Morocco’,‘Syria’,‘Palestine’,‘Iraq’,‘Lybia’) 4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,‘MiddleSchool’,‘HighSchool’) 5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12’) 6 Section ID- classroom student belongs (nominal:‘A’,‘B’,‘C’) 7 Topic- course topic (nominal:‘English’,‘Spanish’, ‘French’,‘Arabic’,‘IT’,‘Math’,‘Chemistry’, ‘Biology’, ‘Science’,‘History’,‘Quran’,‘Geology’) 8 Semester- school year semester (nominal:‘First’,‘Second’) 9 Parent responsible for student (nominal:‘mom’,‘father’) 10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100) 11- Visited resources- how many times the student visits a course content(numeric:0-100) 12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100) 13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100) 14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:‘Yes’,‘No’) 15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:‘Yes’,‘No’) 16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)
The students are classified into three numerical intervals based on their total grade/mark:
Low-Level: interval includes values from 0 to 69, Middle-Level: interval includes values from 70 to 89, High-Level: interval includes values from 90-100.
We will use R Studio to analyze this datasets in R programming language, and aim to achieve the following targets:
- Pre-process of the datasets. Clean the dataset, remove the useless columns or rows; and
- Explore the datasets. Explore the distribution of the datasets in different features: gender, nationality, grade, topic, parental satisfaction etc. (like girls raises more hand, more discussions in high school etc.); and
- Find underlying relationships. Like parent who are not satisfied and not answer survey, connection with study activity and performance (raising hand, discussion, absence, parental satisfaction, answering survey etc.); and
- Build prediction model, like decision tree or regression model to predict the student’s academic performance.
- Evaluate the predictive results of models and improve the models by comparing the accuracy.
Task 1. Exploration of the students’ academic performance datasets
# install libraries
library(ggplot2)
library(reshape2)
library(partykit)## Warning: package 'partykit' was built under R version 3.4.2## Loading required package: gridlibrary(caret)## Loading required package: latticelibrary(e1071)
library(randomForest)## Warning: package 'randomForest' was built under R version 3.4.2## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
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## marginlibrary(dplyr)## Warning: package 'dplyr' was built under R version 3.4.2##
## Attaching package: 'dplyr'## The following object is masked from 'package:randomForest':
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## combine## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionTask 1.1. Preprocess the data set:
# Import XAPI Educational data sets
E <- read.csv("data/xAPI-Edu-Data.csv")
E## gender NationalITy PlaceofBirth StageID GradeID SectionID
## 1 M KW KuwaIT lowerlevel G-04 A
## 2 M KW KuwaIT lowerlevel G-04 A
## 3 M KW KuwaIT lowerlevel G-04 A
## 4 M KW KuwaIT lowerlevel G-04 A
## 5 M KW KuwaIT lowerlevel G-04 A
## 6 F KW KuwaIT lowerlevel G-04 A
## 7 M KW KuwaIT MiddleSchool G-07 A
## 8 M KW KuwaIT MiddleSchool G-07 A
## 9 F KW KuwaIT MiddleSchool G-07 A
## 10 F KW KuwaIT MiddleSchool G-07 B
## 11 M KW KuwaIT MiddleSchool G-07 A
## 12 M KW KuwaIT MiddleSchool G-07 B
## 13 M KW KuwaIT lowerlevel G-04 A
## 14 M lebanon lebanon MiddleSchool G-08 A
## 15 F KW KuwaIT MiddleSchool G-08 A
## 16 F KW KuwaIT MiddleSchool G-06 A
## 17 M KW KuwaIT MiddleSchool G-07 B
## 18 M KW KuwaIT MiddleSchool G-07 A
## 19 F KW KuwaIT MiddleSchool G-07 A
## 20 M KW KuwaIT MiddleSchool G-07 B
## 21 F KW KuwaIT MiddleSchool G-07 A
## 22 F KW KuwaIT MiddleSchool G-07 B
## 23 M KW KuwaIT MiddleSchool G-07 A
## 24 M KW KuwaIT MiddleSchool G-07 A
## 25 M KW KuwaIT MiddleSchool G-07 B
## 26 M KW KuwaIT MiddleSchool G-07 A
## 27 M KW KuwaIT MiddleSchool G-07 B
## 28 M KW KuwaIT MiddleSchool G-08 A
## 29 M KW KuwaIT MiddleSchool G-08 A
## 30 F KW KuwaIT MiddleSchool G-08 A
## 31 F KW KuwaIT MiddleSchool G-08 A
## 32 M KW KuwaIT MiddleSchool G-07 A
## 33 F KW KuwaIT lowerlevel G-07 A
## 34 M KW KuwaIT lowerlevel G-05 A
## 35 M KW KuwaIT MiddleSchool G-07 B
## 36 M KW KuwaIT MiddleSchool G-07 A
## 37 M KW KuwaIT MiddleSchool G-07 B
## 38 F Egypt Egypt MiddleSchool G-07 A
## 39 M KW KuwaIT MiddleSchool G-06 A
## 40 F SaudiArabia SaudiArabia MiddleSchool G-07 B
## 41 F KW KuwaIT MiddleSchool G-07 A
## 42 M KW KuwaIT MiddleSchool G-07 A
## 43 M KW KuwaIT HighSchool G-09 A
## 44 F KW KuwaIT HighSchool G-09 A
## 45 F KW KuwaIT HighSchool G-09 A
## 46 M KW KuwaIT MiddleSchool G-07 A
## 47 M KW KuwaIT lowerlevel G-05 A
## 48 F KW KuwaIT HighSchool G-12 A
## 49 F KW KuwaIT HighSchool G-12 A
## 50 F KW KuwaIT HighSchool G-12 A
## 51 F KW KuwaIT HighSchool G-12 A
## 52 M KW KuwaIT HighSchool G-12 A
## 53 F KW KuwaIT HighSchool G-12 A
## 54 F KW KuwaIT HighSchool G-11 A
## 55 M KW KuwaIT HighSchool G-12 A
## 56 M KW KuwaIT MiddleSchool G-07 A
## 57 M USA USA MiddleSchool G-08 B
## 58 M KW KuwaIT MiddleSchool G-07 A
## 59 M KW KuwaIT MiddleSchool G-07 A
## 60 F USA USA MiddleSchool G-07 A
## 61 F Jordan Jordan lowerlevel G-05 A
## 62 M KW KuwaIT HighSchool G-10 A
## 63 M venzuela venzuela HighSchool G-10 A
## 64 M KW KuwaIT HighSchool G-10 A
## 65 M KW KuwaIT HighSchool G-10 A
## 66 M KW KuwaIT HighSchool G-12 A
## 67 F KW KuwaIT HighSchool G-12 A
## 68 F USA USA HighSchool G-12 A
## 69 F USA USA HighSchool G-12 A
## 70 M Jordan Jordan MiddleSchool G-07 A
## 71 M Jordan Jordan MiddleSchool G-07 A
## 72 M KW KuwaIT MiddleSchool G-07 A
## 73 M KW KuwaIT MiddleSchool G-07 A
## 74 F KW KuwaIT MiddleSchool G-07 A
## 75 M KW KuwaIT MiddleSchool G-07 B
## 76 M Jordan Jordan MiddleSchool G-07 A
## 77 M Iran Iran HighSchool G-09 A
## 78 M KW KuwaIT HighSchool G-09 A
## 79 M KW KuwaIT HighSchool G-11 A
## 80 F lebanon lebanon MiddleSchool G-07 B
## 81 M KW KuwaIT MiddleSchool G-07 A
## 82 M KW KuwaIT lowerlevel G-02 A
## 83 M KW KuwaIT lowerlevel G-02 B
## 84 M KW KuwaIT lowerlevel G-02 B
## 85 M KW KuwaIT lowerlevel G-02 B
## 86 M KW KuwaIT lowerlevel G-02 B
## 87 M SaudiArabia SaudiArabia lowerlevel G-02 B
## 88 M KW KuwaIT lowerlevel G-02 B
## 89 F KW KuwaIT lowerlevel G-02 B
## 90 M KW KuwaIT lowerlevel G-02 B
## 91 M KW KuwaIT lowerlevel G-02 B
## 92 M KW KuwaIT lowerlevel G-02 B
## 93 F KW KuwaIT lowerlevel G-02 B
## 94 M USA USA lowerlevel G-02 B
## 95 F KW KuwaIT lowerlevel G-02 B
## 96 F KW KuwaIT lowerlevel G-02 B
## 97 F KW KuwaIT lowerlevel G-02 C
## 98 F KW KuwaIT lowerlevel G-02 B
## 99 F KW KuwaIT lowerlevel G-02 C
## 100 M KW KuwaIT lowerlevel G-02 B
## 101 M KW KuwaIT lowerlevel G-02 B
## 102 F KW KuwaIT lowerlevel G-02 B
## 103 F KW KuwaIT lowerlevel G-02 B
## 104 M KW KuwaIT lowerlevel G-02 B
## 105 M KW KuwaIT lowerlevel G-02 B
## 106 F KW KuwaIT lowerlevel G-02 B
## 107 F KW KuwaIT lowerlevel G-02 B
## 108 M KW KuwaIT lowerlevel G-02 B
## 109 M SaudiArabia SaudiArabia lowerlevel G-02 B
## 110 M KW KuwaIT lowerlevel G-02 B
## 111 F Jordan Jordan lowerlevel G-02 B
## 112 M KW KuwaIT lowerlevel G-02 C
## 113 M KW KuwaIT lowerlevel G-02 B
## 114 M KW KuwaIT lowerlevel G-02 B
## 115 M KW KuwaIT lowerlevel G-02 B
## 116 M KW KuwaIT lowerlevel G-02 B
## 117 F KW KuwaIT lowerlevel G-02 C
## 118 F KW KuwaIT lowerlevel G-02 C
## 119 F KW KuwaIT lowerlevel G-02 C
## 120 M KW KuwaIT lowerlevel G-02 C
## 121 M KW KuwaIT lowerlevel G-02 C
## 122 M KW KuwaIT lowerlevel G-02 C
## 123 F KW KuwaIT lowerlevel G-02 B
## 124 F lebanon lebanon lowerlevel G-02 B
## 125 M KW KuwaIT lowerlevel G-02 C
## 126 M KW KuwaIT lowerlevel G-02 B
## 127 F Iran Iran lowerlevel G-02 C
## 128 M KW KuwaIT lowerlevel G-02 B
## 129 M KW KuwaIT lowerlevel G-02 C
## 130 M KW KuwaIT lowerlevel G-02 B
## 131 F KW KuwaIT lowerlevel G-02 C
## 132 M Jordan Jordan lowerlevel G-02 C
## 133 M KW KuwaIT lowerlevel G-02 C
## 134 M KW KuwaIT lowerlevel G-02 A
## 135 M KW KuwaIT MiddleSchool G-07 A
## 136 F Egypt Egypt MiddleSchool G-07 A
## 137 F KW USA HighSchool G-11 A
## 138 F KW USA HighSchool G-11 A
## 139 F Jordan Egypt MiddleSchool G-07 A
## 140 M Jordan USA MiddleSchool G-07 A
## 141 M Tunis Tunis MiddleSchool G-07 A
## 142 M Tunis Tunis MiddleSchool G-07 A
## 143 M Tunis Tunis MiddleSchool G-07 A
## 144 M KW KuwaIT MiddleSchool G-07 A
## 145 M KW KuwaIT MiddleSchool G-07 A
## 146 M Tunis SaudiArabia MiddleSchool G-07 A
## 147 F lebanon lebanon MiddleSchool G-07 A
## 148 F lebanon lebanon MiddleSchool G-07 A
## 149 F Jordan Jordan MiddleSchool G-07 A
## 150 F Jordan lebanon MiddleSchool G-07 A
## 151 M SaudiArabia SaudiArabia HighSchool G-11 A
## 152 M SaudiArabia USA HighSchool G-11 A
## 153 F SaudiArabia USA HighSchool G-11 B
## 154 M SaudiArabia USA HighSchool G-11 B
## 155 M SaudiArabia USA HighSchool G-11 B
## 156 M SaudiArabia USA HighSchool G-11 B
## 157 F Tunis USA HighSchool G-11 B
## 158 M Tunis Tunis HighSchool G-11 B
## 159 M Tunis KuwaIT MiddleSchool G-07 B
## 160 F KW KuwaIT MiddleSchool G-07 B
## 161 F Jordan Jordan MiddleSchool G-07 B
## 162 M Jordan Jordan MiddleSchool G-07 B
## 163 F Jordan Jordan lowerlevel G-02 B
## 164 F Jordan USA lowerlevel G-02 B
## 165 M Jordan Jordan lowerlevel G-02 B
## 166 F Morocco Morocco lowerlevel G-02 B
## 167 M KW KuwaIT lowerlevel G-02 B
## 168 M Tunis Tunis lowerlevel G-02 B
## 169 M KW KuwaIT lowerlevel G-02 B
## 170 M KW KuwaIT lowerlevel G-02 B
## 171 M KW KuwaIT lowerlevel G-02 B
## 172 M KW KuwaIT lowerlevel G-02 B
## 173 M Iran Iran lowerlevel G-02 B
## 174 M Syria Syria lowerlevel G-02 B
## 175 F Jordan Jordan lowerlevel G-02 B
## 176 M Iran Iran lowerlevel G-02 B
## 177 M Tunis Tunis lowerlevel G-02 B
## 178 F USA USA lowerlevel G-02 B
## 179 F lebanon lebanon lowerlevel G-02 B
## 180 M lebanon lebanon lowerlevel G-02 B
## 181 F SaudiArabia SaudiArabia lowerlevel G-02 B
## 182 M Jordan Jordan lowerlevel G-02 B
## 183 M KW KuwaIT MiddleSchool G-08 A
## 184 F lebanon lebanon MiddleSchool G-08 A
## 185 M lebanon lebanon MiddleSchool G-08 A
## 186 M lebanon Jordan MiddleSchool G-08 A
## 187 M KW KuwaIT MiddleSchool G-08 A
## 188 F KW KuwaIT MiddleSchool G-08 A
## 189 M KW KuwaIT MiddleSchool G-08 A
## 190 F KW KuwaIT MiddleSchool G-08 A
## 191 M KW KuwaIT MiddleSchool G-08 A
## 192 M KW KuwaIT MiddleSchool G-08 A
## 193 M KW KuwaIT MiddleSchool G-08 A
## 194 M Morocco Morocco MiddleSchool G-08 A
## 195 M Morocco Morocco MiddleSchool G-08 A
## 196 M KW KuwaIT MiddleSchool G-08 A
## 197 M KW KuwaIT MiddleSchool G-08 A
## 198 M KW KuwaIT MiddleSchool G-08 A
## 199 M Morocco Morocco MiddleSchool G-08 B
## 200 M KW KuwaIT MiddleSchool G-08 B
## 201 M KW KuwaIT MiddleSchool G-08 B
## 202 F lebanon lebanon MiddleSchool G-08 A
## 203 M KW KuwaIT MiddleSchool G-08 B
## 204 F KW KuwaIT MiddleSchool G-08 B
## 205 M Jordan Jordan MiddleSchool G-08 B
## 206 F SaudiArabia SaudiArabia MiddleSchool G-08 B
## 207 M KW KuwaIT MiddleSchool G-08 B
## 208 M KW KuwaIT MiddleSchool G-08 C
## 209 M Tunis Tunis MiddleSchool G-08 C
## 210 M KW KuwaIT MiddleSchool G-08 C
## 211 M Jordan SaudiArabia MiddleSchool G-08 C
## 212 M KW KuwaIT MiddleSchool G-08 C
## 213 M Tunis Tunis MiddleSchool G-08 C
## 214 M Tunis Tunis MiddleSchool G-08 C
## 215 M KW KuwaIT MiddleSchool G-08 C
## 216 M KW KuwaIT MiddleSchool G-08 C
## 217 M Iran Iran MiddleSchool G-08 C
## 218 F Jordan KuwaIT MiddleSchool G-08 C
## 219 M KW KuwaIT MiddleSchool G-08 C
## 220 M KW KuwaIT MiddleSchool G-08 C
## 221 M lebanon lebanon MiddleSchool G-08 C
## 222 M lebanon lebanon MiddleSchool G-08 C
## 223 M KW KuwaIT MiddleSchool G-08 B
## 224 M KW KuwaIT MiddleSchool G-08 A
## 225 M KW KuwaIT MiddleSchool G-08 B
## 226 M KW KuwaIT MiddleSchool G-08 A
## 227 M KW KuwaIT MiddleSchool G-08 B
## 228 M KW KuwaIT MiddleSchool G-08 A
## 229 M KW KuwaIT HighSchool G-11 B
## 230 M KW KuwaIT MiddleSchool G-08 A
## 231 M Iran Iran MiddleSchool G-08 A
## 232 M Syria Jordan MiddleSchool G-08 A
## 233 M Syria Syria MiddleSchool G-07 A
## 234 F Jordan Jordan MiddleSchool G-07 B
## 235 M KW KuwaIT MiddleSchool G-07 B
## 236 M KW KuwaIT MiddleSchool G-07 B
## 237 M Jordan Jordan MiddleSchool G-07 B
## 238 M KW KuwaIT MiddleSchool G-07 B
## 239 F KW KuwaIT MiddleSchool G-07 B
## 240 M Egypt Egypt MiddleSchool G-07 B
## 241 M KW KuwaIT MiddleSchool G-07 B
## 242 M Egypt Egypt MiddleSchool G-07 B
## 243 M KW KuwaIT MiddleSchool G-07 B
## 244 M Egypt KuwaIT MiddleSchool G-07 B
## 245 M KW KuwaIT MiddleSchool G-07 B
## 246 F KW KuwaIT MiddleSchool G-06 A
## 247 F KW KuwaIT MiddleSchool G-06 A
## 248 F KW KuwaIT MiddleSchool G-06 A
## 249 F KW KuwaIT MiddleSchool G-06 A
## 250 M KW KuwaIT lowerlevel G-04 A
## 251 M KW KuwaIT lowerlevel G-04 A
## 252 M Egypt Egypt lowerlevel G-04 A
## 253 F KW KuwaIT lowerlevel G-04 A
## 254 M KW KuwaIT lowerlevel G-04 A
## 255 M KW KuwaIT lowerlevel G-04 A
## 256 M KW KuwaIT lowerlevel G-04 A
## 257 F KW KuwaIT lowerlevel G-04 A
## 258 F KW KuwaIT lowerlevel G-04 A
## 259 F KW KuwaIT lowerlevel G-04 A
## 260 M KW KuwaIT lowerlevel G-04 A
## 261 M KW KuwaIT lowerlevel G-04 A
## 262 M KW KuwaIT lowerlevel G-04 A
## 263 F KW KuwaIT lowerlevel G-04 A
## 264 M Egypt Egypt lowerlevel G-04 A
## 265 F Jordan Jordan MiddleSchool G-06 A
## 266 F Jordan Jordan MiddleSchool G-06 A
## 267 M Jordan Jordan MiddleSchool G-06 A
## 268 M Jordan Jordan MiddleSchool G-06 A
## 269 M Jordan Jordan MiddleSchool G-06 A
## 270 M Jordan Jordan MiddleSchool G-06 A
## 271 F Jordan Jordan MiddleSchool G-06 A
## 272 F Jordan Jordan MiddleSchool G-06 A
## 273 M Jordan Jordan MiddleSchool G-06 A
## 274 M Jordan Jordan MiddleSchool G-06 A
## 275 F lebanon lebanon MiddleSchool G-06 A
## 276 F lebanon lebanon MiddleSchool G-06 A
## 277 M Palestine Jordan MiddleSchool G-06 A
## 278 M Palestine Jordan MiddleSchool G-06 A
## 279 M Palestine Jordan MiddleSchool G-06 A
## 280 M Palestine Jordan MiddleSchool G-06 A
## 281 M Iraq Iraq MiddleSchool G-06 A
## 282 M Iraq Iraq MiddleSchool G-06 A
## 283 F Jordan Jordan MiddleSchool G-06 A
## 284 F Jordan Jordan MiddleSchool G-06 A
## 285 M Jordan Jordan MiddleSchool G-06 A
## 286 M Jordan Jordan MiddleSchool G-06 A
## 287 F Jordan Jordan MiddleSchool G-06 A
## 288 F Jordan Jordan MiddleSchool G-06 A
## 289 M Palestine Jordan MiddleSchool G-06 A
## 290 M Palestine Jordan MiddleSchool G-06 A
## 291 F Jordan Jordan lowerlevel G-04 A
## 292 F Jordan Jordan lowerlevel G-04 A
## 293 F Palestine Palestine lowerlevel G-04 A
## 294 F Palestine Palestine lowerlevel G-04 A
## 295 M Iraq Iraq lowerlevel G-04 A
## 296 M Iraq Iraq lowerlevel G-04 A
## 297 F Jordan Jordan lowerlevel G-04 A
## 298 F Jordan Jordan lowerlevel G-04 A
## 299 M Jordan Jordan lowerlevel G-04 A
## 300 M Jordan Jordan lowerlevel G-04 A
## 301 M Jordan Jordan lowerlevel G-04 A
## 302 M Jordan Jordan lowerlevel G-04 A
## 303 F Jordan Jordan lowerlevel G-04 B
## 304 F Jordan Jordan lowerlevel G-04 B
## 305 F Jordan Jordan lowerlevel G-04 B
## 306 F Jordan Jordan lowerlevel G-04 B
## 307 M Jordan Jordan lowerlevel G-04 B
## 308 M Jordan Jordan lowerlevel G-04 B
## 309 M Palestine Jordan lowerlevel G-04 B
## 310 M Palestine Jordan lowerlevel G-04 B
## 311 M Jordan Jordan lowerlevel G-04 B
## 312 M Jordan Jordan lowerlevel G-04 B
## 313 F Jordan Jordan lowerlevel G-04 B
## 314 F Jordan Jordan lowerlevel G-04 B
## 315 F Jordan lebanon lowerlevel G-04 B
## 316 F Jordan lebanon lowerlevel G-04 B
## 317 M Jordan Jordan lowerlevel G-02 A
## 318 M Jordan Jordan lowerlevel G-02 A
## 319 F Jordan Jordan lowerlevel G-02 A
## 320 F Jordan Jordan lowerlevel G-02 A
## 321 F Jordan Jordan lowerlevel G-02 A
## 322 F Jordan Jordan lowerlevel G-02 A
## 323 M Jordan Jordan lowerlevel G-02 A
## 324 M Jordan Jordan lowerlevel G-02 A
## 325 M Syria Syria lowerlevel G-02 A
## 326 M Syria Syria lowerlevel G-02 A
## 327 M Jordan Jordan lowerlevel G-02 A
## 328 M Jordan Jordan lowerlevel G-02 A
## 329 F Jordan Palestine lowerlevel G-02 A
## 330 F Jordan Palestine lowerlevel G-02 A
## 331 M Jordan Jordan lowerlevel G-02 A
## 332 M Jordan Jordan lowerlevel G-02 A
## 333 F Jordan Jordan lowerlevel G-02 A
## 334 F Jordan Jordan lowerlevel G-02 A
## 335 M Lybia Lybia lowerlevel G-02 A
## 336 M Lybia Lybia lowerlevel G-02 A
## 337 F Palestine Jordan lowerlevel G-02 A
## 338 F Palestine Jordan lowerlevel G-02 A
## 339 F Palestine Jordan lowerlevel G-02 B
## 340 F Palestine Jordan lowerlevel G-02 B
## 341 F Iraq Iraq lowerlevel G-02 B
## 342 F Iraq Iraq lowerlevel G-02 B
## 343 M Jordan Jordan lowerlevel G-02 B
## 344 M Jordan Jordan lowerlevel G-02 B
## 345 F Jordan Jordan lowerlevel G-02 B
## 346 F Jordan Jordan lowerlevel G-02 B
## 347 F Jordan Jordan lowerlevel G-02 B
## 348 F Jordan Jordan lowerlevel G-02 B
## 349 M Lybia Lybia lowerlevel G-02 B
## 350 M Lybia Lybia lowerlevel G-02 B
## 351 M Egypt Egypt lowerlevel G-02 B
## 352 M Egypt Egypt lowerlevel G-02 B
## 353 F Jordan Jordan lowerlevel G-02 B
## 354 F Jordan Jordan lowerlevel G-02 B
## 355 M Jordan Jordan lowerlevel G-02 B
## 356 M Jordan Jordan lowerlevel G-02 B
## 357 M Jordan Jordan lowerlevel G-02 B
## 358 M Jordan Jordan lowerlevel G-02 B
## 359 F lebanon lebanon lowerlevel G-02 B
## 360 F lebanon lebanon lowerlevel G-02 B
## 361 M Jordan Jordan lowerlevel G-02 A
## 362 M Jordan Jordan lowerlevel G-02 A
## 363 M Palestine Jordan lowerlevel G-02 A
## 364 M Palestine Jordan lowerlevel G-02 A
## 365 M Iraq Iraq lowerlevel G-02 A
## 366 M Iraq Iraq lowerlevel G-02 A
## 367 M Jordan Jordan lowerlevel G-02 A
## 368 M Jordan Jordan lowerlevel G-02 A
## 369 F Palestine Jordan lowerlevel G-02 A
## 370 F Palestine Jordan lowerlevel G-02 A
## 371 M Jordan Jordan lowerlevel G-02 A
## 372 M Jordan Jordan lowerlevel G-02 A
## 373 F Jordan Jordan lowerlevel G-02 A
## 374 F Jordan Jordan lowerlevel G-02 A
## 375 M Jordan Jordan lowerlevel G-02 B
## 376 M Jordan Jordan lowerlevel G-02 B
## 377 M Jordan Jordan lowerlevel G-02 B
## 378 M Jordan Jordan lowerlevel G-02 B
## 379 M Jordan Jordan lowerlevel G-02 B
## 380 M Jordan Jordan lowerlevel G-02 B
## 381 F Jordan Jordan lowerlevel G-02 B
## 382 F Jordan Jordan lowerlevel G-02 B
## 383 M Iraq Iraq lowerlevel G-02 B
## 384 M Iraq Iraq lowerlevel G-02 B
## 385 F Iraq Iraq lowerlevel G-02 B
## 386 F Iraq Iraq lowerlevel G-02 B
## 387 M Jordan Jordan lowerlevel G-02 B
## 388 M Jordan Jordan lowerlevel G-02 B
## 389 M Jordan SaudiArabia lowerlevel G-02 B
## 390 M Jordan SaudiArabia lowerlevel G-02 B
## 391 F Jordan Jordan MiddleSchool G-07 A
## 392 F Jordan Jordan MiddleSchool G-07 A
## 393 F Iraq Iraq MiddleSchool G-07 A
## 394 F Iraq Iraq MiddleSchool G-07 A
## 395 M Jordan Palestine MiddleSchool G-07 A
## 396 M Jordan Palestine MiddleSchool G-07 A
## 397 M Jordan Jordan MiddleSchool G-07 A
## 398 M Jordan Jordan MiddleSchool G-07 A
## 399 M Palestine Palestine MiddleSchool G-07 A
## 400 M Palestine Palestine MiddleSchool G-07 A
## 401 M Jordan Jordan MiddleSchool G-07 A
## 402 M Jordan Jordan MiddleSchool G-07 A
## 403 M Jordan Jordan MiddleSchool G-07 A
## 404 M Jordan Jordan MiddleSchool G-07 A
## 405 F Syria Syria MiddleSchool G-07 A
## 406 F Syria Syria MiddleSchool G-07 A
## 407 M Jordan Jordan MiddleSchool G-07 A
## 408 M Jordan Jordan MiddleSchool G-07 A
## 409 M Jordan Jordan MiddleSchool G-07 B
## 410 M Jordan Jordan MiddleSchool G-07 B
## 411 F Jordan Jordan MiddleSchool G-07 B
## 412 F Jordan Jordan MiddleSchool G-07 B
## 413 M Palestine Jordan MiddleSchool G-07 B
## 414 M Palestine Jordan MiddleSchool G-07 B
## 415 F Lybia Lybia MiddleSchool G-07 B
## 416 F Lybia Lybia MiddleSchool G-07 B
## 417 M Iraq Iraq MiddleSchool G-07 B
## 418 M Iraq Iraq MiddleSchool G-07 B
## 419 M Palestine Jordan MiddleSchool G-07 B
## 420 M Palestine Jordan MiddleSchool G-07 B
## 421 F Jordan Jordan MiddleSchool G-08 A
## 422 F Jordan Jordan MiddleSchool G-08 A
## 423 F Jordan Jordan MiddleSchool G-08 A
## 424 F Jordan Jordan MiddleSchool G-08 A
## 425 F Jordan Jordan MiddleSchool G-08 A
## 426 F Jordan Jordan MiddleSchool G-08 A
## 427 M Jordan Jordan MiddleSchool G-08 A
## 428 M Jordan Jordan MiddleSchool G-08 A
## 429 M Jordan SaudiArabia MiddleSchool G-08 A
## 430 M Jordan SaudiArabia MiddleSchool G-08 A
## 431 M Jordan SaudiArabia MiddleSchool G-08 A
## 432 M Jordan SaudiArabia MiddleSchool G-08 A
## 433 M Jordan SaudiArabia MiddleSchool G-08 A
## 434 M Jordan SaudiArabia MiddleSchool G-08 A
## 435 M Jordan Jordan MiddleSchool G-08 A
## 436 M Jordan Jordan MiddleSchool G-08 A
## 437 M Jordan Jordan MiddleSchool G-08 A
## 438 M Jordan Jordan MiddleSchool G-08 A
## 439 M Jordan Jordan MiddleSchool G-08 A
## 440 M Jordan Jordan MiddleSchool G-08 A
## 441 M Jordan Jordan MiddleSchool G-08 A
## 442 M Jordan Jordan MiddleSchool G-08 A
## 443 M Jordan Jordan MiddleSchool G-08 A
## 444 M Jordan Jordan MiddleSchool G-08 A
## 445 M Jordan Jordan MiddleSchool G-08 A
## 446 M Jordan Jordan MiddleSchool G-08 A
## 447 M Jordan Jordan MiddleSchool G-08 A
## 448 M Jordan Jordan MiddleSchool G-08 A
## 449 M Jordan Jordan MiddleSchool G-08 A
## 450 M Jordan Jordan MiddleSchool G-08 A
## 451 F Jordan Jordan MiddleSchool G-08 A
## 452 F Jordan Jordan MiddleSchool G-08 A
## 453 F Jordan Jordan MiddleSchool G-08 A
## 454 F Jordan Jordan MiddleSchool G-08 A
## 455 F Jordan Jordan MiddleSchool G-08 A
## 456 F Jordan Jordan MiddleSchool G-08 A
## 457 F Jordan Jordan MiddleSchool G-08 A
## 458 F Jordan Jordan MiddleSchool G-08 A
## 459 M Iraq Iraq MiddleSchool G-08 A
## 460 M Iraq Iraq MiddleSchool G-08 A
## 461 M Iraq Iraq MiddleSchool G-08 A
## 462 M Iraq Iraq MiddleSchool G-08 A
## 463 M Iraq Iraq MiddleSchool G-08 A
## 464 M Iraq Iraq MiddleSchool G-08 A
## 465 F Jordan Jordan MiddleSchool G-08 A
## 466 F Jordan Jordan MiddleSchool G-08 A
## 467 F Jordan Jordan MiddleSchool G-08 A
## 468 F Jordan Jordan MiddleSchool G-08 A
## 469 F Jordan Jordan MiddleSchool G-08 A
## 470 F Jordan Jordan MiddleSchool G-08 A
## 471 M Palestine Jordan MiddleSchool G-08 A
## 472 M Palestine Jordan MiddleSchool G-08 A
## 473 M Palestine Palestine MiddleSchool G-08 A
## 474 M Palestine Palestine MiddleSchool G-08 A
## 475 F Jordan Jordan MiddleSchool G-08 A
## 476 F Jordan Jordan MiddleSchool G-08 A
## 477 F Jordan Jordan MiddleSchool G-08 A
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## AnnouncementsView Discussion ParentAnsweringSurvey
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## 3 0 30 No
## 4 5 35 No
## 5 12 50 No
## 6 13 70 Yes
## 7 0 17 No
## 8 15 22 Yes
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## 11 30 80 Yes
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## 13 0 11 No
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## 16 22 66 Yes
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## 21 33 90 No
## 22 4 80 No
## 23 2 90 No
## 24 2 50 No
## 25 3 70 Yes
## 26 30 40 Yes
## 27 25 40 Yes
## 28 12 33 No
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## 30 33 35 No
## 31 50 70 Yes
## 32 40 16 Yes
## 33 10 50 Yes
## 34 9 40 No
## 35 8 40 No
## 36 17 30 No
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## 39 0 4 No
## 40 37 70 Yes
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## 58 6 5 Yes
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## 407 10 1 No
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## 415 2 1 No
## 416 9 3 No
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## 429 2 41 No
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## 475 4 8 No
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## ParentschoolSatisfaction StudentAbsenceDays Class
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## 41 Bad Above-7 L
## 42 Good Under-7 M
## 43 Good Above-7 L
## 44 Good Under-7 M
## 45 Bad Under-7 M
## 46 Good Above-7 L
## 47 Bad Above-7 L
## 48 Good Under-7 H
## 49 Good Under-7 H
## 50 Good Under-7 M
## 51 Bad Above-7 L
## 52 Good Above-7 L
## 53 Bad Under-7 M
## 54 Good Under-7 H
## 55 Bad Above-7 L
## 56 Good Above-7 L
## 57 Good Above-7 L
## 58 Good Above-7 L
## 59 Good Under-7 M
## 60 Good Under-7 M
## 61 Good Above-7 L
## 62 Good Under-7 M
## 63 Good Under-7 H
## 64 Bad Under-7 M
## 65 Bad Above-7 L
## 66 Bad Above-7 L
## 67 Bad Above-7 M
## 68 Good Under-7 H
## 69 Good Under-7 H
## 70 Bad Under-7 M
## 71 Good Above-7 L
## 72 Good Under-7 M
## 73 Bad Under-7 L
## 74 Bad Above-7 M
## 75 Bad Under-7 L
## 76 Good Under-7 M
## 77 Good Under-7 M
## 78 Good Under-7 M
## 79 Bad Above-7 L
## 80 Bad Under-7 H
## 81 Bad Under-7 L
## 82 Bad Above-7 L
## 83 Good Above-7 L
## 84 Good Above-7 M
## 85 Good Above-7 H
## 86 Bad Above-7 L
## 87 Good Under-7 H
## 88 Bad Under-7 L
## 89 Bad Under-7 L
## 90 Good Above-7 L
## 91 Good Above-7 L
## 92 Good Under-7 H
## 93 Good Under-7 H
## 94 Good Under-7 H
## 95 Good Above-7 L
## 96 Bad Under-7 H
## 97 Good Under-7 H
## 98 Bad Under-7 M
## 99 Bad Under-7 M
## 100 Bad Above-7 M
## 101 Bad Under-7 M
## 102 Good Under-7 H
## 103 Bad Above-7 L
## 104 Bad Above-7 L
## 105 Good Above-7 M
## 106 Good Above-7 L
## 107 Good Under-7 M
## 108 Bad Under-7 H
## 109 Bad Above-7 M
## 110 Bad Under-7 M
## 111 Good Under-7 H
## 112 Good Under-7 M
## 113 Bad Above-7 L
## 114 Bad Above-7 L
## 115 Bad Above-7 L
## 116 Good Above-7 L
## 117 Good Above-7 M
## 118 Good Under-7 H
## 119 Good Above-7 M
## 120 Bad Under-7 M
## 121 Good Under-7 M
## 122 Bad Above-7 L
## 123 Good Above-7 M
## 124 Good Under-7 M
## 125 Bad Under-7 L
## 126 Good Above-7 L
## 127 Good Under-7 M
## 128 Bad Above-7 L
## 129 Bad Under-7 L
## 130 Good Under-7 L
## 131 Bad Above-7 L
## 132 Good Under-7 M
## 133 Bad Above-7 L
## 134 Good Above-7 L
## 135 Good Under-7 H
## 136 Good Under-7 H
## 137 Good Under-7 H
## 138 Bad Under-7 M
## 139 Bad Under-7 H
## 140 Bad Under-7 M
## 141 Bad Above-7 L
## 142 Bad Above-7 L
## 143 Good Under-7 M
## 144 Good Under-7 H
## 145 Bad Above-7 L
## 146 Bad Under-7 M
## 147 Good Under-7 H
## 148 Bad Under-7 M
## 149 Bad Under-7 M
## 150 Bad Under-7 H
## 151 Good Under-7 H
## 152 Good Under-7 M
## 153 Bad Under-7 H
## 154 Bad Above-7 L
## 155 Bad Above-7 M
## 156 Bad Under-7 H
## 157 Good Under-7 M
## 158 Good Under-7 M
## 159 Bad Above-7 L
## 160 Good Under-7 M
## 161 Good Under-7 H
## 162 Bad Under-7 M
## 163 Bad Under-7 H
## 164 Bad Above-7 M
## 165 Good Above-7 M
## 166 Good Under-7 H
## 167 Bad Under-7 M
## 168 Good Under-7 H
## 169 Good Under-7 H
## 170 Bad Under-7 M
## 171 Good Under-7 H
## 172 Good Under-7 M
## 173 Good Above-7 L
## 174 Good Above-7 L
## 175 Bad Above-7 M
## 176 Bad Above-7 L
## 177 Bad Under-7 H
## 178 Bad Under-7 M
## 179 Bad Under-7 H
## 180 Bad Under-7 M
## 181 Bad Under-7 H
## 182 Bad Above-7 L
## 183 Bad Under-7 H
## 184 Bad Under-7 M
## 185 Bad Above-7 L
## 186 Good Under-7 H
## 187 Good Under-7 M
## 188 Good Under-7 M
## 189 Good Under-7 H
## 190 Bad Under-7 M
## 191 Bad Under-7 L
## 192 Good Above-7 L
## 193 Good Under-7 M
## 194 Bad Under-7 M
## 195 Bad Under-7 M
## 196 Good Under-7 M
## 197 Good Under-7 H
## 198 Bad Under-7 H
## 199 Bad Above-7 L
## 200 Bad Under-7 M
## 201 Bad Under-7 H
## 202 Good Under-7 H
## 203 Good Under-7 M
## 204 Bad Under-7 M
## 205 Bad Above-7 L
## 206 Bad Above-7 H
## 207 Good Under-7 M
## 208 Good Under-7 M
## 209 Bad Under-7 M
## 210 Bad Under-7 M
## 211 Good Under-7 H
## 212 Bad Under-7 M
## 213 Good Under-7 H
## 214 Bad Above-7 L
## 215 Bad Above-7 L
## 216 Good Above-7 L
## 217 Bad Above-7 M
## 218 Good Under-7 M
## 219 Bad Under-7 H
## 220 Good Under-7 M
## 221 Good Under-7 M
## 222 Good Under-7 M
## 223 Bad Under-7 M
## 224 Good Under-7 H
## 225 Bad Under-7 H
## 226 Bad Under-7 M
## 227 Bad Under-7 L
## 228 Good Under-7 L
## 229 Good Above-7 H
## 230 Bad Above-7 L
## 231 Bad Above-7 M
## 232 Good Above-7 L
## 233 Good Above-7 M
## 234 Good Above-7 M
## 235 Good Above-7 M
## 236 Bad Above-7 L
## 237 Bad Above-7 L
## 238 Good Above-7 M
## 239 Good Above-7 M
## 240 Good Under-7 H
## 241 Bad Under-7 H
## 242 Good Under-7 M
## 243 Bad Above-7 L
## 244 Bad Above-7 M
## 245 Bad Under-7 H
## 246 Bad Under-7 M
## 247 Good Under-7 H
## 248 Bad Above-7 M
## 249 Bad Above-7 L
## 250 Good Above-7 M
## 251 Good Under-7 H
## 252 Good Above-7 L
## 253 Bad Under-7 M
## 254 Bad Above-7 L
## 255 Good Under-7 H
## 256 Good Under-7 H
## 257 Good Under-7 H
## 258 Good Under-7 M
## 259 Good Under-7 M
## 260 Bad Above-7 L
## 261 Bad Above-7 L
## 262 Bad Above-7 M
## 263 Good Above-7 M
## 264 Bad Above-7 M
## 265 Bad Under-7 M
## 266 Bad Under-7 H
## 267 Good Above-7 M
## 268 Good Above-7 M
## 269 Bad Above-7 M
## 270 Bad Above-7 M
## 271 Good Under-7 H
## 272 Good Under-7 M
## 273 Bad Under-7 M
## 274 Bad Under-7 H
## 275 Good Under-7 H
## 276 Good Under-7 H
## 277 Good Under-7 H
## 278 Good Under-7 H
## 279 Good Under-7 M
## 280 Good Under-7 M
## 281 Good Under-7 H
## 282 Good Under-7 H
## 283 Good Under-7 H
## 284 Good Under-7 H
## 285 Bad Above-7 M
## 286 Bad Above-7 M
## 287 Good Under-7 H
## 288 Good Under-7 H
## 289 Good Under-7 M
## 290 Good Under-7 M
## 291 Bad Above-7 L
## 292 Bad Above-7 L
## 293 Good Under-7 H
## 294 Good Under-7 H
## 295 Good Above-7 M
## 296 Good Above-7 M
## 297 Good Under-7 H
## 298 Good Under-7 H
## 299 Good Above-7 M
## 300 Good Above-7 M
## 301 Bad Above-7 L
## 302 Bad Above-7 L
## 303 Bad Under-7 M
## 304 Bad Under-7 M
## 305 Bad Under-7 M
## 306 Bad Under-7 M
## 307 Good Under-7 H
## 308 Good Under-7 H
## 309 Good Above-7 M
## 310 Good Above-7 M
## 311 Good Above-7 M
## 312 Good Above-7 M
## 313 Good Under-7 H
## 314 Good Under-7 H
## 315 Good Under-7 H
## 316 Good Under-7 H
## 317 Good Under-7 H
## 318 Good Under-7 H
## 319 Good Under-7 H
## 320 Good Under-7 H
## 321 Good Above-7 M
## 322 Good Above-7 M
## 323 Bad Above-7 L
## 324 Bad Above-7 L
## 325 Bad Under-7 M
## 326 Bad Under-7 M
## 327 Bad Above-7 L
## 328 Bad Above-7 L
## 329 Good Under-7 M
## 330 Good Under-7 M
## 331 Good Above-7 L
## 332 Good Above-7 L
## 333 Good Under-7 M
## 334 Good Under-7 M
## 335 Good Above-7 L
## 336 Good Above-7 L
## 337 Good Under-7 M
## 338 Good Under-7 M
## 339 Good Under-7 M
## 340 Good Under-7 M
## 341 Good Under-7 M
## 342 Good Under-7 M
## 343 Bad Under-7 M
## 344 Bad Under-7 M
## 345 Good Under-7 H
## 346 Good Under-7 H
## 347 Good Under-7 H
## 348 Good Under-7 H
## 349 Good Above-7 L
## 350 Good Above-7 L
## 351 Good Above-7 L
## 352 Good Above-7 L
## 353 Good Under-7 M
## 354 Good Under-7 M
## 355 Good Under-7 H
## 356 Good Under-7 H
## 357 Good Under-7 M
## 358 Good Under-7 M
## 359 Good Under-7 H
## 360 Good Under-7 H
## 361 Good Above-7 M
## 362 Good Above-7 M
## 363 Good Under-7 H
## 364 Good Under-7 H
## 365 Good Under-7 H
## 366 Good Under-7 H
## 367 Good Above-7 M
## 368 Good Above-7 M
## 369 Good Under-7 H
## 370 Good Under-7 H
## 371 Bad Under-7 M
## 372 Bad Under-7 M
## 373 Bad Above-7 L
## 374 Bad Above-7 L
## 375 Bad Above-7 L
## 376 Bad Above-7 L
## 377 Bad Above-7 M
## 378 Bad Above-7 M
## 379 Bad Above-7 L
## 380 Bad Above-7 L
## 381 Bad Above-7 L
## 382 Bad Above-7 L
## 383 Good Under-7 H
## 384 Good Under-7 H
## 385 Good Under-7 H
## 386 Good Under-7 H
## 387 Good Under-7 M
## 388 Good Under-7 M
## 389 Good Above-7 L
## 390 Good Above-7 L
## 391 Good Above-7 M
## 392 Good Above-7 M
## 393 Good Under-7 H
## 394 Good Under-7 H
## 395 Good Under-7 H
## 396 Good Under-7 H
## 397 Bad Under-7 M
## 398 Bad Under-7 M
## 399 Good Under-7 H
## 400 Good Under-7 H
## 401 Good Above-7 M
## 402 Good Above-7 M
## 403 Good Under-7 H
## 404 Good Under-7 H
## 405 Good Under-7 H
## 406 Good Under-7 H
## 407 Bad Above-7 L
## 408 Bad Above-7 L
## 409 Bad Under-7 M
## 410 Bad Under-7 M
## 411 Good Under-7 H
## 412 Good Under-7 H
## 413 Good Under-7 M
## 414 Good Under-7 M
## 415 Good Above-7 L
## 416 Good Above-7 L
## 417 Good Under-7 H
## 418 Good Under-7 H
## 419 Good Under-7 H
## 420 Good Under-7 H
## 421 Good Under-7 H
## 422 Good Under-7 H
## 423 Good Above-7 M
## 424 Good Above-7 M
## 425 Good Under-7 H
## 426 Good Under-7 H
## 427 Good Above-7 M
## 428 Good Above-7 M
## 429 Bad Above-7 L
## 430 Bad Above-7 L
## 431 Bad Under-7 H
## 432 Bad Under-7 H
## 433 Bad Under-7 M
## 434 Bad Under-7 M
## 435 Good Above-7 M
## 436 Good Above-7 M
## 437 Good Under-7 M
## 438 Good Under-7 M
## 439 Good Under-7 M
## 440 Good Under-7 M
## 441 Good Under-7 M
## 442 Good Under-7 M
## 443 Good Above-7 M
## 444 Good Above-7 M
## 445 Good Above-7 L
## 446 Good Above-7 L
## 447 Good Under-7 M
## 448 Good Under-7 M
## 449 Good Under-7 H
## 450 Good Under-7 H
## 451 Good Under-7 H
## 452 Good Under-7 H
## 453 Good Above-7 M
## 454 Good Above-7 M
## 455 Good Under-7 M
## 456 Good Under-7 M
## 457 Good Under-7 H
## 458 Good Under-7 H
## 459 Good Under-7 H
## 460 Good Under-7 H
## 461 Good Above-7 M
## 462 Good Above-7 M
## 463 Good Above-7 M
## 464 Good Above-7 M
## 465 Good Under-7 H
## 466 Good Under-7 H
## 467 Good Under-7 H
## 468 Good Under-7 H
## 469 Bad Above-7 L
## 470 Bad Above-7 L
## 471 Good Under-7 M
## 472 Good Under-7 M
## 473 Good Under-7 M
## 474 Good Under-7 M
## 475 Bad Above-7 L
## 476 Bad Above-7 L
## 477 Bad Under-7 M
## 478 Bad Under-7 M
## 479 Bad Above-7 L
## 480 Bad Above-7 Lstr(E)## 'data.frame': 480 obs. of 17 variables:
## $ gender : Factor w/ 2 levels "F","M": 2 2 2 2 2 1 2 2 1 1 ...
## $ NationalITy : Factor w/ 14 levels "Egypt","Iran",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ PlaceofBirth : Factor w/ 14 levels "Egypt","Iran",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ StageID : Factor w/ 3 levels "HighSchool","lowerlevel",..: 2 2 2 2 2 2 3 3 3 3 ...
## $ GradeID : Factor w/ 10 levels "G-02","G-04",..: 2 2 2 2 2 2 5 5 5 5 ...
## $ SectionID : Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 1 1 1 2 ...
## $ Topic : Factor w/ 12 levels "Arabic","Biology",..: 8 8 8 8 8 8 9 9 9 8 ...
## $ Semester : Factor w/ 2 levels "F","S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Relation : Factor w/ 2 levels "Father","Mum": 1 1 1 1 1 1 1 1 1 1 ...
## $ raisedhands : int 15 20 10 30 40 42 35 50 12 70 ...
## $ VisITedResources : int 16 20 7 25 50 30 12 10 21 80 ...
## $ AnnouncementsView : int 2 3 0 5 12 13 0 15 16 25 ...
## $ Discussion : int 20 25 30 35 50 70 17 22 50 70 ...
## $ ParentAnsweringSurvey : Factor w/ 2 levels "No","Yes": 2 2 1 1 1 2 1 2 2 2 ...
## $ ParentschoolSatisfaction: Factor w/ 2 levels "Bad","Good": 2 2 1 1 1 1 1 2 2 2 ...
## $ StudentAbsenceDays : Factor w/ 2 levels "Above-7","Under-7": 2 2 1 1 1 1 1 2 2 2 ...
## $ Class : Factor w/ 3 levels "H","L","M": 3 3 2 2 3 3 2 3 3 3 ...summary(E)## gender NationalITy PlaceofBirth StageID
## F:175 KW :179 KuwaIT :180 HighSchool : 33
## M:305 Jordan :172 Jordan :176 lowerlevel :199
## Palestine: 28 Iraq : 22 MiddleSchool:248
## Iraq : 22 lebanon : 19
## lebanon : 17 SaudiArabia: 16
## Tunis : 12 USA : 16
## (Other) : 50 (Other) : 51
## GradeID SectionID Topic Semester Relation
## G-02 :147 A:283 IT : 95 F:245 Father:283
## G-08 :116 B:167 French : 65 S:235 Mum :197
## G-07 :101 C: 30 Arabic : 59
## G-04 : 48 Science: 51
## G-06 : 32 English: 45
## G-11 : 13 Biology: 30
## (Other): 23 (Other):135
## raisedhands VisITedResources AnnouncementsView Discussion
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 1.00
## 1st Qu.: 15.75 1st Qu.:20.0 1st Qu.:14.00 1st Qu.:20.00
## Median : 50.00 Median :65.0 Median :33.00 Median :39.00
## Mean : 46.77 Mean :54.8 Mean :37.92 Mean :43.28
## 3rd Qu.: 75.00 3rd Qu.:84.0 3rd Qu.:58.00 3rd Qu.:70.00
## Max. :100.00 Max. :99.0 Max. :98.00 Max. :99.00
##
## ParentAnsweringSurvey ParentschoolSatisfaction StudentAbsenceDays Class
## No :210 Bad :188 Above-7:191 H:142
## Yes:270 Good:292 Under-7:289 L:127
## M:211
##
##
##
## #Reorder the "Class"" column of the data set, make the sequence of "Class" to be "H","M","L" instead of "H","L","M".
E$Class = factor(E$Class, levels=c("H","M","L"), labels=c("H","M","L"))
#Reorder the "StageID" column of the data set, make the sequence of "StageID" to be "HighSchool","MiddleSchool","Lowerlevel" instead of "HighSchool","lowerlevel","MiddleSchool".
E$StageID = factor(E$StageID, levels=c("HighSchool","MiddleSchool","lowerlevel"), labels=c("HighSchool","MiddleSchool","Lowerlevel"))
E## gender NationalITy PlaceofBirth StageID GradeID SectionID
## 1 M KW KuwaIT Lowerlevel G-04 A
## 2 M KW KuwaIT Lowerlevel G-04 A
## 3 M KW KuwaIT Lowerlevel G-04 A
## 4 M KW KuwaIT Lowerlevel G-04 A
## 5 M KW KuwaIT Lowerlevel G-04 A
## 6 F KW KuwaIT Lowerlevel G-04 A
## 7 M KW KuwaIT MiddleSchool G-07 A
## 8 M KW KuwaIT MiddleSchool G-07 A
## 9 F KW KuwaIT MiddleSchool G-07 A
## 10 F KW KuwaIT MiddleSchool G-07 B
## 11 M KW KuwaIT MiddleSchool G-07 A
## 12 M KW KuwaIT MiddleSchool G-07 B
## 13 M KW KuwaIT Lowerlevel G-04 A
## 14 M lebanon lebanon MiddleSchool G-08 A
## 15 F KW KuwaIT MiddleSchool G-08 A
## 16 F KW KuwaIT MiddleSchool G-06 A
## 17 M KW KuwaIT MiddleSchool G-07 B
## 18 M KW KuwaIT MiddleSchool G-07 A
## 19 F KW KuwaIT MiddleSchool G-07 A
## 20 M KW KuwaIT MiddleSchool G-07 B
## 21 F KW KuwaIT MiddleSchool G-07 A
## 22 F KW KuwaIT MiddleSchool G-07 B
## 23 M KW KuwaIT MiddleSchool G-07 A
## 24 M KW KuwaIT MiddleSchool G-07 A
## 25 M KW KuwaIT MiddleSchool G-07 B
## 26 M KW KuwaIT MiddleSchool G-07 A
## 27 M KW KuwaIT MiddleSchool G-07 B
## 28 M KW KuwaIT MiddleSchool G-08 A
## 29 M KW KuwaIT MiddleSchool G-08 A
## 30 F KW KuwaIT MiddleSchool G-08 A
## 31 F KW KuwaIT MiddleSchool G-08 A
## 32 M KW KuwaIT MiddleSchool G-07 A
## 33 F KW KuwaIT Lowerlevel G-07 A
## 34 M KW KuwaIT Lowerlevel G-05 A
## 35 M KW KuwaIT MiddleSchool G-07 B
## 36 M KW KuwaIT MiddleSchool G-07 A
## 37 M KW KuwaIT MiddleSchool G-07 B
## 38 F Egypt Egypt MiddleSchool G-07 A
## 39 M KW KuwaIT MiddleSchool G-06 A
## 40 F SaudiArabia SaudiArabia MiddleSchool G-07 B
## 41 F KW KuwaIT MiddleSchool G-07 A
## 42 M KW KuwaIT MiddleSchool G-07 A
## 43 M KW KuwaIT HighSchool G-09 A
## 44 F KW KuwaIT HighSchool G-09 A
## 45 F KW KuwaIT HighSchool G-09 A
## 46 M KW KuwaIT MiddleSchool G-07 A
## 47 M KW KuwaIT Lowerlevel G-05 A
## 48 F KW KuwaIT HighSchool G-12 A
## 49 F KW KuwaIT HighSchool G-12 A
## 50 F KW KuwaIT HighSchool G-12 A
## 51 F KW KuwaIT HighSchool G-12 A
## 52 M KW KuwaIT HighSchool G-12 A
## 53 F KW KuwaIT HighSchool G-12 A
## 54 F KW KuwaIT HighSchool G-11 A
## 55 M KW KuwaIT HighSchool G-12 A
## 56 M KW KuwaIT MiddleSchool G-07 A
## 57 M USA USA MiddleSchool G-08 B
## 58 M KW KuwaIT MiddleSchool G-07 A
## 59 M KW KuwaIT MiddleSchool G-07 A
## 60 F USA USA MiddleSchool G-07 A
## 61 F Jordan Jordan Lowerlevel G-05 A
## 62 M KW KuwaIT HighSchool G-10 A
## 63 M venzuela venzuela HighSchool G-10 A
## 64 M KW KuwaIT HighSchool G-10 A
## 65 M KW KuwaIT HighSchool G-10 A
## 66 M KW KuwaIT HighSchool G-12 A
## 67 F KW KuwaIT HighSchool G-12 A
## 68 F USA USA HighSchool G-12 A
## 69 F USA USA HighSchool G-12 A
## 70 M Jordan Jordan MiddleSchool G-07 A
## 71 M Jordan Jordan MiddleSchool G-07 A
## 72 M KW KuwaIT MiddleSchool G-07 A
## 73 M KW KuwaIT MiddleSchool G-07 A
## 74 F KW KuwaIT MiddleSchool G-07 A
## 75 M KW KuwaIT MiddleSchool G-07 B
## 76 M Jordan Jordan MiddleSchool G-07 A
## 77 M Iran Iran HighSchool G-09 A
## 78 M KW KuwaIT HighSchool G-09 A
## 79 M KW KuwaIT HighSchool G-11 A
## 80 F lebanon lebanon MiddleSchool G-07 B
## 81 M KW KuwaIT MiddleSchool G-07 A
## 82 M KW KuwaIT Lowerlevel G-02 A
## 83 M KW KuwaIT Lowerlevel G-02 B
## 84 M KW KuwaIT Lowerlevel G-02 B
## 85 M KW KuwaIT Lowerlevel G-02 B
## 86 M KW KuwaIT Lowerlevel G-02 B
## 87 M SaudiArabia SaudiArabia Lowerlevel G-02 B
## 88 M KW KuwaIT Lowerlevel G-02 B
## 89 F KW KuwaIT Lowerlevel G-02 B
## 90 M KW KuwaIT Lowerlevel G-02 B
## 91 M KW KuwaIT Lowerlevel G-02 B
## 92 M KW KuwaIT Lowerlevel G-02 B
## 93 F KW KuwaIT Lowerlevel G-02 B
## 94 M USA USA Lowerlevel G-02 B
## 95 F KW KuwaIT Lowerlevel G-02 B
## 96 F KW KuwaIT Lowerlevel G-02 B
## 97 F KW KuwaIT Lowerlevel G-02 C
## 98 F KW KuwaIT Lowerlevel G-02 B
## 99 F KW KuwaIT Lowerlevel G-02 C
## 100 M KW KuwaIT Lowerlevel G-02 B
## 101 M KW KuwaIT Lowerlevel G-02 B
## 102 F KW KuwaIT Lowerlevel G-02 B
## 103 F KW KuwaIT Lowerlevel G-02 B
## 104 M KW KuwaIT Lowerlevel G-02 B
## 105 M KW KuwaIT Lowerlevel G-02 B
## 106 F KW KuwaIT Lowerlevel G-02 B
## 107 F KW KuwaIT Lowerlevel G-02 B
## 108 M KW KuwaIT Lowerlevel G-02 B
## 109 M SaudiArabia SaudiArabia Lowerlevel G-02 B
## 110 M KW KuwaIT Lowerlevel G-02 B
## 111 F Jordan Jordan Lowerlevel G-02 B
## 112 M KW KuwaIT Lowerlevel G-02 C
## 113 M KW KuwaIT Lowerlevel G-02 B
## 114 M KW KuwaIT Lowerlevel G-02 B
## 115 M KW KuwaIT Lowerlevel G-02 B
## 116 M KW KuwaIT Lowerlevel G-02 B
## 117 F KW KuwaIT Lowerlevel G-02 C
## 118 F KW KuwaIT Lowerlevel G-02 C
## 119 F KW KuwaIT Lowerlevel G-02 C
## 120 M KW KuwaIT Lowerlevel G-02 C
## 121 M KW KuwaIT Lowerlevel G-02 C
## 122 M KW KuwaIT Lowerlevel G-02 C
## 123 F KW KuwaIT Lowerlevel G-02 B
## 124 F lebanon lebanon Lowerlevel G-02 B
## 125 M KW KuwaIT Lowerlevel G-02 C
## 126 M KW KuwaIT Lowerlevel G-02 B
## 127 F Iran Iran Lowerlevel G-02 C
## 128 M KW KuwaIT Lowerlevel G-02 B
## 129 M KW KuwaIT Lowerlevel G-02 C
## 130 M KW KuwaIT Lowerlevel G-02 B
## 131 F KW KuwaIT Lowerlevel G-02 C
## 132 M Jordan Jordan Lowerlevel G-02 C
## 133 M KW KuwaIT Lowerlevel G-02 C
## 134 M KW KuwaIT Lowerlevel G-02 A
## 135 M KW KuwaIT MiddleSchool G-07 A
## 136 F Egypt Egypt MiddleSchool G-07 A
## 137 F KW USA HighSchool G-11 A
## 138 F KW USA HighSchool G-11 A
## 139 F Jordan Egypt MiddleSchool G-07 A
## 140 M Jordan USA MiddleSchool G-07 A
## 141 M Tunis Tunis MiddleSchool G-07 A
## 142 M Tunis Tunis MiddleSchool G-07 A
## 143 M Tunis Tunis MiddleSchool G-07 A
## 144 M KW KuwaIT MiddleSchool G-07 A
## 145 M KW KuwaIT MiddleSchool G-07 A
## 146 M Tunis SaudiArabia MiddleSchool G-07 A
## 147 F lebanon lebanon MiddleSchool G-07 A
## 148 F lebanon lebanon MiddleSchool G-07 A
## 149 F Jordan Jordan MiddleSchool G-07 A
## 150 F Jordan lebanon MiddleSchool G-07 A
## 151 M SaudiArabia SaudiArabia HighSchool G-11 A
## 152 M SaudiArabia USA HighSchool G-11 A
## 153 F SaudiArabia USA HighSchool G-11 B
## 154 M SaudiArabia USA HighSchool G-11 B
## 155 M SaudiArabia USA HighSchool G-11 B
## 156 M SaudiArabia USA HighSchool G-11 B
## 157 F Tunis USA HighSchool G-11 B
## 158 M Tunis Tunis HighSchool G-11 B
## 159 M Tunis KuwaIT MiddleSchool G-07 B
## 160 F KW KuwaIT MiddleSchool G-07 B
## 161 F Jordan Jordan MiddleSchool G-07 B
## 162 M Jordan Jordan MiddleSchool G-07 B
## 163 F Jordan Jordan Lowerlevel G-02 B
## 164 F Jordan USA Lowerlevel G-02 B
## 165 M Jordan Jordan Lowerlevel G-02 B
## 166 F Morocco Morocco Lowerlevel G-02 B
## 167 M KW KuwaIT Lowerlevel G-02 B
## 168 M Tunis Tunis Lowerlevel G-02 B
## 169 M KW KuwaIT Lowerlevel G-02 B
## 170 M KW KuwaIT Lowerlevel G-02 B
## 171 M KW KuwaIT Lowerlevel G-02 B
## 172 M KW KuwaIT Lowerlevel G-02 B
## 173 M Iran Iran Lowerlevel G-02 B
## 174 M Syria Syria Lowerlevel G-02 B
## 175 F Jordan Jordan Lowerlevel G-02 B
## 176 M Iran Iran Lowerlevel G-02 B
## 177 M Tunis Tunis Lowerlevel G-02 B
## 178 F USA USA Lowerlevel G-02 B
## 179 F lebanon lebanon Lowerlevel G-02 B
## 180 M lebanon lebanon Lowerlevel G-02 B
## 181 F SaudiArabia SaudiArabia Lowerlevel G-02 B
## 182 M Jordan Jordan Lowerlevel G-02 B
## 183 M KW KuwaIT MiddleSchool G-08 A
## 184 F lebanon lebanon MiddleSchool G-08 A
## 185 M lebanon lebanon MiddleSchool G-08 A
## 186 M lebanon Jordan MiddleSchool G-08 A
## 187 M KW KuwaIT MiddleSchool G-08 A
## 188 F KW KuwaIT MiddleSchool G-08 A
## 189 M KW KuwaIT MiddleSchool G-08 A
## 190 F KW KuwaIT MiddleSchool G-08 A
## 191 M KW KuwaIT MiddleSchool G-08 A
## 192 M KW KuwaIT MiddleSchool G-08 A
## 193 M KW KuwaIT MiddleSchool G-08 A
## 194 M Morocco Morocco MiddleSchool G-08 A
## 195 M Morocco Morocco MiddleSchool G-08 A
## 196 M KW KuwaIT MiddleSchool G-08 A
## 197 M KW KuwaIT MiddleSchool G-08 A
## 198 M KW KuwaIT MiddleSchool G-08 A
## 199 M Morocco Morocco MiddleSchool G-08 B
## 200 M KW KuwaIT MiddleSchool G-08 B
## 201 M KW KuwaIT MiddleSchool G-08 B
## 202 F lebanon lebanon MiddleSchool G-08 A
## 203 M KW KuwaIT MiddleSchool G-08 B
## 204 F KW KuwaIT MiddleSchool G-08 B
## 205 M Jordan Jordan MiddleSchool G-08 B
## 206 F SaudiArabia SaudiArabia MiddleSchool G-08 B
## 207 M KW KuwaIT MiddleSchool G-08 B
## 208 M KW KuwaIT MiddleSchool G-08 C
## 209 M Tunis Tunis MiddleSchool G-08 C
## 210 M KW KuwaIT MiddleSchool G-08 C
## 211 M Jordan SaudiArabia MiddleSchool G-08 C
## 212 M KW KuwaIT MiddleSchool G-08 C
## 213 M Tunis Tunis MiddleSchool G-08 C
## 214 M Tunis Tunis MiddleSchool G-08 C
## 215 M KW KuwaIT MiddleSchool G-08 C
## 216 M KW KuwaIT MiddleSchool G-08 C
## 217 M Iran Iran MiddleSchool G-08 C
## 218 F Jordan KuwaIT MiddleSchool G-08 C
## 219 M KW KuwaIT MiddleSchool G-08 C
## 220 M KW KuwaIT MiddleSchool G-08 C
## 221 M lebanon lebanon MiddleSchool G-08 C
## 222 M lebanon lebanon MiddleSchool G-08 C
## 223 M KW KuwaIT MiddleSchool G-08 B
## 224 M KW KuwaIT MiddleSchool G-08 A
## 225 M KW KuwaIT MiddleSchool G-08 B
## 226 M KW KuwaIT MiddleSchool G-08 A
## 227 M KW KuwaIT MiddleSchool G-08 B
## 228 M KW KuwaIT MiddleSchool G-08 A
## 229 M KW KuwaIT HighSchool G-11 B
## 230 M KW KuwaIT MiddleSchool G-08 A
## 231 M Iran Iran MiddleSchool G-08 A
## 232 M Syria Jordan MiddleSchool G-08 A
## 233 M Syria Syria MiddleSchool G-07 A
## 234 F Jordan Jordan MiddleSchool G-07 B
## 235 M KW KuwaIT MiddleSchool G-07 B
## 236 M KW KuwaIT MiddleSchool G-07 B
## 237 M Jordan Jordan MiddleSchool G-07 B
## 238 M KW KuwaIT MiddleSchool G-07 B
## 239 F KW KuwaIT MiddleSchool G-07 B
## 240 M Egypt Egypt MiddleSchool G-07 B
## 241 M KW KuwaIT MiddleSchool G-07 B
## 242 M Egypt Egypt MiddleSchool G-07 B
## 243 M KW KuwaIT MiddleSchool G-07 B
## 244 M Egypt KuwaIT MiddleSchool G-07 B
## 245 M KW KuwaIT MiddleSchool G-07 B
## 246 F KW KuwaIT MiddleSchool G-06 A
## 247 F KW KuwaIT MiddleSchool G-06 A
## 248 F KW KuwaIT MiddleSchool G-06 A
## 249 F KW KuwaIT MiddleSchool G-06 A
## 250 M KW KuwaIT Lowerlevel G-04 A
## 251 M KW KuwaIT Lowerlevel G-04 A
## 252 M Egypt Egypt Lowerlevel G-04 A
## 253 F KW KuwaIT Lowerlevel G-04 A
## 254 M KW KuwaIT Lowerlevel G-04 A
## 255 M KW KuwaIT Lowerlevel G-04 A
## 256 M KW KuwaIT Lowerlevel G-04 A
## 257 F KW KuwaIT Lowerlevel G-04 A
## 258 F KW KuwaIT Lowerlevel G-04 A
## 259 F KW KuwaIT Lowerlevel G-04 A
## 260 M KW KuwaIT Lowerlevel G-04 A
## 261 M KW KuwaIT Lowerlevel G-04 A
## 262 M KW KuwaIT Lowerlevel G-04 A
## 263 F KW KuwaIT Lowerlevel G-04 A
## 264 M Egypt Egypt Lowerlevel G-04 A
## 265 F Jordan Jordan MiddleSchool G-06 A
## 266 F Jordan Jordan MiddleSchool G-06 A
## 267 M Jordan Jordan MiddleSchool G-06 A
## 268 M Jordan Jordan MiddleSchool G-06 A
## 269 M Jordan Jordan MiddleSchool G-06 A
## 270 M Jordan Jordan MiddleSchool G-06 A
## 271 F Jordan Jordan MiddleSchool G-06 A
## 272 F Jordan Jordan MiddleSchool G-06 A
## 273 M Jordan Jordan MiddleSchool G-06 A
## 274 M Jordan Jordan MiddleSchool G-06 A
## 275 F lebanon lebanon MiddleSchool G-06 A
## 276 F lebanon lebanon MiddleSchool G-06 A
## 277 M Palestine Jordan MiddleSchool G-06 A
## 278 M Palestine Jordan MiddleSchool G-06 A
## 279 M Palestine Jordan MiddleSchool G-06 A
## 280 M Palestine Jordan MiddleSchool G-06 A
## 281 M Iraq Iraq MiddleSchool G-06 A
## 282 M Iraq Iraq MiddleSchool G-06 A
## 283 F Jordan Jordan MiddleSchool G-06 A
## 284 F Jordan Jordan MiddleSchool G-06 A
## 285 M Jordan Jordan MiddleSchool G-06 A
## 286 M Jordan Jordan MiddleSchool G-06 A
## 287 F Jordan Jordan MiddleSchool G-06 A
## 288 F Jordan Jordan MiddleSchool G-06 A
## 289 M Palestine Jordan MiddleSchool G-06 A
## 290 M Palestine Jordan MiddleSchool G-06 A
## 291 F Jordan Jordan Lowerlevel G-04 A
## 292 F Jordan Jordan Lowerlevel G-04 A
## 293 F Palestine Palestine Lowerlevel G-04 A
## 294 F Palestine Palestine Lowerlevel G-04 A
## 295 M Iraq Iraq Lowerlevel G-04 A
## 296 M Iraq Iraq Lowerlevel G-04 A
## 297 F Jordan Jordan Lowerlevel G-04 A
## 298 F Jordan Jordan Lowerlevel G-04 A
## 299 M Jordan Jordan Lowerlevel G-04 A
## 300 M Jordan Jordan Lowerlevel G-04 A
## 301 M Jordan Jordan Lowerlevel G-04 A
## 302 M Jordan Jordan Lowerlevel G-04 A
## 303 F Jordan Jordan Lowerlevel G-04 B
## 304 F Jordan Jordan Lowerlevel G-04 B
## 305 F Jordan Jordan Lowerlevel G-04 B
## 306 F Jordan Jordan Lowerlevel G-04 B
## 307 M Jordan Jordan Lowerlevel G-04 B
## 308 M Jordan Jordan Lowerlevel G-04 B
## 309 M Palestine Jordan Lowerlevel G-04 B
## 310 M Palestine Jordan Lowerlevel G-04 B
## 311 M Jordan Jordan Lowerlevel G-04 B
## 312 M Jordan Jordan Lowerlevel G-04 B
## 313 F Jordan Jordan Lowerlevel G-04 B
## 314 F Jordan Jordan Lowerlevel G-04 B
## 315 F Jordan lebanon Lowerlevel G-04 B
## 316 F Jordan lebanon Lowerlevel G-04 B
## 317 M Jordan Jordan Lowerlevel G-02 A
## 318 M Jordan Jordan Lowerlevel G-02 A
## 319 F Jordan Jordan Lowerlevel G-02 A
## 320 F Jordan Jordan Lowerlevel G-02 A
## 321 F Jordan Jordan Lowerlevel G-02 A
## 322 F Jordan Jordan Lowerlevel G-02 A
## 323 M Jordan Jordan Lowerlevel G-02 A
## 324 M Jordan Jordan Lowerlevel G-02 A
## 325 M Syria Syria Lowerlevel G-02 A
## 326 M Syria Syria Lowerlevel G-02 A
## 327 M Jordan Jordan Lowerlevel G-02 A
## 328 M Jordan Jordan Lowerlevel G-02 A
## 329 F Jordan Palestine Lowerlevel G-02 A
## 330 F Jordan Palestine Lowerlevel G-02 A
## 331 M Jordan Jordan Lowerlevel G-02 A
## 332 M Jordan Jordan Lowerlevel G-02 A
## 333 F Jordan Jordan Lowerlevel G-02 A
## 334 F Jordan Jordan Lowerlevel G-02 A
## 335 M Lybia Lybia Lowerlevel G-02 A
## 336 M Lybia Lybia Lowerlevel G-02 A
## 337 F Palestine Jordan Lowerlevel G-02 A
## 338 F Palestine Jordan Lowerlevel G-02 A
## 339 F Palestine Jordan Lowerlevel G-02 B
## 340 F Palestine Jordan Lowerlevel G-02 B
## 341 F Iraq Iraq Lowerlevel G-02 B
## 342 F Iraq Iraq Lowerlevel G-02 B
## 343 M Jordan Jordan Lowerlevel G-02 B
## 344 M Jordan Jordan Lowerlevel G-02 B
## 345 F Jordan Jordan Lowerlevel G-02 B
## 346 F Jordan Jordan Lowerlevel G-02 B
## 347 F Jordan Jordan Lowerlevel G-02 B
## 348 F Jordan Jordan Lowerlevel G-02 B
## 349 M Lybia Lybia Lowerlevel G-02 B
## 350 M Lybia Lybia Lowerlevel G-02 B
## 351 M Egypt Egypt Lowerlevel G-02 B
## 352 M Egypt Egypt Lowerlevel G-02 B
## 353 F Jordan Jordan Lowerlevel G-02 B
## 354 F Jordan Jordan Lowerlevel G-02 B
## 355 M Jordan Jordan Lowerlevel G-02 B
## 356 M Jordan Jordan Lowerlevel G-02 B
## 357 M Jordan Jordan Lowerlevel G-02 B
## 358 M Jordan Jordan Lowerlevel G-02 B
## 359 F lebanon lebanon Lowerlevel G-02 B
## 360 F lebanon lebanon Lowerlevel G-02 B
## 361 M Jordan Jordan Lowerlevel G-02 A
## 362 M Jordan Jordan Lowerlevel G-02 A
## 363 M Palestine Jordan Lowerlevel G-02 A
## 364 M Palestine Jordan Lowerlevel G-02 A
## 365 M Iraq Iraq Lowerlevel G-02 A
## 366 M Iraq Iraq Lowerlevel G-02 A
## 367 M Jordan Jordan Lowerlevel G-02 A
## 368 M Jordan Jordan Lowerlevel G-02 A
## 369 F Palestine Jordan Lowerlevel G-02 A
## 370 F Palestine Jordan Lowerlevel G-02 A
## 371 M Jordan Jordan Lowerlevel G-02 A
## 372 M Jordan Jordan Lowerlevel G-02 A
## 373 F Jordan Jordan Lowerlevel G-02 A
## 374 F Jordan Jordan Lowerlevel G-02 A
## 375 M Jordan Jordan Lowerlevel G-02 B
## 376 M Jordan Jordan Lowerlevel G-02 B
## 377 M Jordan Jordan Lowerlevel G-02 B
## 378 M Jordan Jordan Lowerlevel G-02 B
## 379 M Jordan Jordan Lowerlevel G-02 B
## 380 M Jordan Jordan Lowerlevel G-02 B
## 381 F Jordan Jordan Lowerlevel G-02 B
## 382 F Jordan Jordan Lowerlevel G-02 B
## 383 M Iraq Iraq Lowerlevel G-02 B
## 384 M Iraq Iraq Lowerlevel G-02 B
## 385 F Iraq Iraq Lowerlevel G-02 B
## 386 F Iraq Iraq Lowerlevel G-02 B
## 387 M Jordan Jordan Lowerlevel G-02 B
## 388 M Jordan Jordan Lowerlevel G-02 B
## 389 M Jordan SaudiArabia Lowerlevel G-02 B
## 390 M Jordan SaudiArabia Lowerlevel G-02 B
## 391 F Jordan Jordan MiddleSchool G-07 A
## 392 F Jordan Jordan MiddleSchool G-07 A
## 393 F Iraq Iraq MiddleSchool G-07 A
## 394 F Iraq Iraq MiddleSchool G-07 A
## 395 M Jordan Palestine MiddleSchool G-07 A
## 396 M Jordan Palestine MiddleSchool G-07 A
## 397 M Jordan Jordan MiddleSchool G-07 A
## 398 M Jordan Jordan MiddleSchool G-07 A
## 399 M Palestine Palestine MiddleSchool G-07 A
## 400 M Palestine Palestine MiddleSchool G-07 A
## 401 M Jordan Jordan MiddleSchool G-07 A
## 402 M Jordan Jordan MiddleSchool G-07 A
## 403 M Jordan Jordan MiddleSchool G-07 A
## 404 M Jordan Jordan MiddleSchool G-07 A
## 405 F Syria Syria MiddleSchool G-07 A
## 406 F Syria Syria MiddleSchool G-07 A
## 407 M Jordan Jordan MiddleSchool G-07 A
## 408 M Jordan Jordan MiddleSchool G-07 A
## 409 M Jordan Jordan MiddleSchool G-07 B
## 410 M Jordan Jordan MiddleSchool G-07 B
## 411 F Jordan Jordan MiddleSchool G-07 B
## 412 F Jordan Jordan MiddleSchool G-07 B
## 413 M Palestine Jordan MiddleSchool G-07 B
## 414 M Palestine Jordan MiddleSchool G-07 B
## 415 F Lybia Lybia MiddleSchool G-07 B
## 416 F Lybia Lybia MiddleSchool G-07 B
## 417 M Iraq Iraq MiddleSchool G-07 B
## 418 M Iraq Iraq MiddleSchool G-07 B
## 419 M Palestine Jordan MiddleSchool G-07 B
## 420 M Palestine Jordan MiddleSchool G-07 B
## 421 F Jordan Jordan MiddleSchool G-08 A
## 422 F Jordan Jordan MiddleSchool G-08 A
## 423 F Jordan Jordan MiddleSchool G-08 A
## 424 F Jordan Jordan MiddleSchool G-08 A
## 425 F Jordan Jordan MiddleSchool G-08 A
## 426 F Jordan Jordan MiddleSchool G-08 A
## 427 M Jordan Jordan MiddleSchool G-08 A
## 428 M Jordan Jordan MiddleSchool G-08 A
## 429 M Jordan SaudiArabia MiddleSchool G-08 A
## 430 M Jordan SaudiArabia MiddleSchool G-08 A
## 431 M Jordan SaudiArabia MiddleSchool G-08 A
## 432 M Jordan SaudiArabia MiddleSchool G-08 A
## 433 M Jordan SaudiArabia MiddleSchool G-08 A
## 434 M Jordan SaudiArabia MiddleSchool G-08 A
## 435 M Jordan Jordan MiddleSchool G-08 A
## 436 M Jordan Jordan MiddleSchool G-08 A
## 437 M Jordan Jordan MiddleSchool G-08 A
## 438 M Jordan Jordan MiddleSchool G-08 A
## 439 M Jordan Jordan MiddleSchool G-08 A
## 440 M Jordan Jordan MiddleSchool G-08 A
## 441 M Jordan Jordan MiddleSchool G-08 A
## 442 M Jordan Jordan MiddleSchool G-08 A
## 443 M Jordan Jordan MiddleSchool G-08 A
## 444 M Jordan Jordan MiddleSchool G-08 A
## 445 M Jordan Jordan MiddleSchool G-08 A
## 446 M Jordan Jordan MiddleSchool G-08 A
## 447 M Jordan Jordan MiddleSchool G-08 A
## 448 M Jordan Jordan MiddleSchool G-08 A
## 449 M Jordan Jordan MiddleSchool G-08 A
## 450 M Jordan Jordan MiddleSchool G-08 A
## 451 F Jordan Jordan MiddleSchool G-08 A
## 452 F Jordan Jordan MiddleSchool G-08 A
## 453 F Jordan Jordan MiddleSchool G-08 A
## 454 F Jordan Jordan MiddleSchool G-08 A
## 455 F Jordan Jordan MiddleSchool G-08 A
## 456 F Jordan Jordan MiddleSchool G-08 A
## 457 F Jordan Jordan MiddleSchool G-08 A
## 458 F Jordan Jordan MiddleSchool G-08 A
## 459 M Iraq Iraq MiddleSchool G-08 A
## 460 M Iraq Iraq MiddleSchool G-08 A
## 461 M Iraq Iraq MiddleSchool G-08 A
## 462 M Iraq Iraq MiddleSchool G-08 A
## 463 M Iraq Iraq MiddleSchool G-08 A
## 464 M Iraq Iraq MiddleSchool G-08 A
## 465 F Jordan Jordan MiddleSchool G-08 A
## 466 F Jordan Jordan MiddleSchool G-08 A
## 467 F Jordan Jordan MiddleSchool G-08 A
## 468 F Jordan Jordan MiddleSchool G-08 A
## 469 F Jordan Jordan MiddleSchool G-08 A
## 470 F Jordan Jordan MiddleSchool G-08 A
## 471 M Palestine Jordan MiddleSchool G-08 A
## 472 M Palestine Jordan MiddleSchool G-08 A
## 473 M Palestine Palestine MiddleSchool G-08 A
## 474 M Palestine Palestine MiddleSchool G-08 A
## 475 F Jordan Jordan MiddleSchool G-08 A
## 476 F Jordan Jordan MiddleSchool G-08 A
## 477 F Jordan Jordan MiddleSchool G-08 A
## 478 F Jordan Jordan MiddleSchool G-08 A
## 479 F Jordan Jordan MiddleSchool G-08 A
## 480 F Jordan Jordan MiddleSchool G-08 A
## Topic Semester Relation raisedhands VisITedResources
## 1 IT F Father 15 16
## 2 IT F Father 20 20
## 3 IT F Father 10 7
## 4 IT F Father 30 25
## 5 IT F Father 40 50
## 6 IT F Father 42 30
## 7 Math F Father 35 12
## 8 Math F Father 50 10
## 9 Math F Father 12 21
## 10 IT F Father 70 80
## 11 Math F Father 50 88
## 12 Math F Father 19 6
## 13 IT F Father 5 1
## 14 Math F Father 20 14
## 15 Math F Mum 62 70
## 16 IT F Father 30 40
## 17 IT F Father 36 30
## 18 Math F Father 55 13
## 19 IT F Mum 69 15
## 20 IT F Mum 70 50
## 21 IT F Father 60 60
## 22 IT F Father 10 12
## 23 IT F Father 15 21
## 24 IT F Father 2 0
## 25 IT F Father 0 2
## 26 IT F Father 8 7
## 27 IT F Father 19 19
## 28 Arabic F Father 25 15
## 29 Science F Father 75 85
## 30 Arabic F Father 30 90
## 31 Arabic F Father 35 80
## 32 IT F Father 4 5
## 33 IT F Father 2 19
## 34 English F Father 8 22
## 35 Science F Father 12 11
## 36 English F Father 10 12
## 37 Science F Mum 8 6
## 38 IT F Father 45 54
## 39 IT F Father 0 0
## 40 Science F Father 50 90
## 41 IT F Father 14 13
## 42 IT F Father 19 20
## 43 IT F Father 10 12
## 44 IT F Father 30 35
## 45 IT F Father 33 33
## 46 Quran F Father 20 12
## 47 English F Father 7 10
## 48 English F Mum 70 4
## 49 English F Mum 13 80
## 50 English F Father 29 39
## 51 English F Father 20 14
## 52 English F Father 39 15
## 53 English F Father 55 90
## 54 Science F Mum 49 70
## 55 English F Mum 12 50
## 56 Math F Father 16 14
## 57 Math F Father 19 5
## 58 Math F Father 5 2
## 59 Math F Father 28 60
## 60 Math F Father 27 22
## 61 English F Mum 21 10
## 62 IT F Father 50 70
## 63 IT F Mum 80 90
## 64 IT F Father 17 13
## 65 IT F Father 0 5
## 66 English F Father 13 5
## 67 English F Father 25 10
## 68 English F Mum 65 75
## 69 IT F Mum 70 69
## 70 IT F Father 39 40
## 71 IT F Father 22 30
## 72 IT F Father 29 22
## 73 Math F Father 11 2
## 74 English F Father 19 30
## 75 IT F Father 12 0
## 76 Quran F Mum 50 90
## 77 IT F Mum 15 70
## 78 IT F Father 20 80
## 79 Quran F Father 13 3
## 80 Math F Mum 80 90
## 81 Math F Father 8 15
## 82 IT F Father 8 25
## 83 IT F Father 7 5
## 84 IT F Father 7 4
## 85 IT F Father 50 70
## 86 IT F Father 1 0
## 87 IT F Father 70 12
## 88 IT F Father 19 70
## 89 IT F Father 3 12
## 90 IT F Father 5 20
## 91 IT F Father 4 8
## 92 IT F Father 80 90
## 93 IT F Father 50 70
## 94 IT F Father 55 89
## 95 IT F Father 80 44
## 96 IT F Father 100 80
## 97 IT F Father 14 60
## 98 IT F Father 6 2
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## 100 IT F Father 50 7
## 101 IT F Father 50 90
## 102 IT F Mum 70 92
## 103 IT F Father 2 6
## 104 IT F Father 1 7
## 105 IT F Father 0 12
## 106 IT F Father 0 0
## 107 IT F Father 12 26
## 108 IT F Father 70 90
## 109 IT F Father 7 12
## 110 IT F Father 90 70
## 111 IT F Mum 70 88
## 112 IT F Father 77 80
## 113 IT F Father 2 5
## 114 IT F Father 25 27
## 115 IT F Father 11 2
## 116 IT F Father 0 8
## 117 IT F Mum 77 80
## 118 IT F Mum 25 29
## 119 IT F Mum 24 35
## 120 IT F Father 60 60
## 121 IT F Mum 21 12
## 122 IT F Mum 0 4
## 123 IT F Father 66 90
## 124 IT F Father 70 98
## 125 IT F Father 0 6
## 126 IT F Father 12 30
## 127 IT F Father 2 9
## 128 IT F Father 55 33
## 129 IT F Father 12 10
## 130 IT F Father 70 90
## 131 IT F Father 7 9
## 132 IT F Father 80 42
## 133 IT S Father 0 3
## 134 IT S Father 12 60
## 135 Quran S Father 80 80
## 136 Quran S Mum 70 80
## 137 Quran S Mum 70 80
## 138 Quran F Mum 60 80
## 139 Quran F Mum 100 80
## 140 Quran F Mum 100 85
## 141 Quran F Father 10 60
## 142 Quran S Father 19 65
## 143 Quran S Father 10 75
## 144 Quran S Father 80 90
## 145 Quran S Father 10 10
## 146 Quran S Father 70 75
## 147 Quran S Mum 100 75
## 148 Quran S Father 10 79
## 149 Quran S Father 60 55
## 150 Quran S Mum 100 75
## 151 Quran S Father 80 80
## 152 Science S Father 23 63
## 153 Science S Father 100 91
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## 155 English S Father 70 50
## 156 Math S Father 70 58
## 157 French S Father 70 50
## 158 Science S Father 70 50
## 159 Science S Father 22 51
## 160 Science S Father 62 68
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## 190 Arabic S Father 85 66
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## 194 Arabic S Mum 81 75
## 195 Arabic S Mum 53 75
## 196 Arabic S Father 15 43
## 197 Arabic S Mum 92 65
## 198 Arabic S Mum 83 75
## 199 Arabic S Father 27 15
## 200 Arabic S Father 45 95
## 201 Arabic S Mum 15 90
## 202 Arabic S Mum 45 58
## 203 Arabic S Father 25 5
## 204 Arabic S Father 22 51
## 205 Arabic S Father 29 10
## 206 Arabic S Mum 72 51
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## 209 Spanish S Mum 27 41
## 210 Spanish S Father 70 81
## 211 Spanish S Mum 27 90
## 212 Spanish S Father 17 61
## 213 Spanish S Mum 87 81
## 214 Spanish S Father 7 61
## 215 Spanish S Father 17 50
## 216 Spanish S Father 5 21
## 217 Spanish S Mum 27 41
## 218 Spanish S Father 87 88
## 219 Spanish S Mum 96 61
## 220 Spanish S Mum 57 51
## 221 Spanish S Father 77 69
## 222 Spanish S Father 80 51
## 223 Spanish S Father 62 61
## 224 Spanish S Mum 72 83
## 225 Spanish S Mum 87 81
## 226 Spanish S Mum 72 90
## 227 Spanish S Father 2 11
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## 231 Spanish S Mum 51 42
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## 234 Science S Mum 32 80
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## 236 Science S Father 12 8
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## 239 Science S Mum 72 80
## 240 Science S Mum 92 80
## 241 Science S Mum 72 80
## 242 Science S Father 72 80
## 243 Science S Father 22 20
## 244 Science S Father 12 20
## 245 IT S Mum 70 92
## 246 IT S Father 50 40
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## 248 Math S Father 87 48
## 249 Quran S Father 70 40
## 250 IT S Father 65 86
## 251 Math S Father 15 6
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## 266 English S Mum 79 84
## 267 English F Father 19 80
## 268 English S Father 11 70
## 269 English F Father 10 8
## 270 English S Father 18 28
## 271 English F Mum 90 84
## 272 English S Mum 92 81
## 273 English F Father 42 21
## 274 English S Father 22 20
## 275 English F Mum 95 82
## 276 English S Mum 90 62
## 277 English F Mum 72 21
## 278 English S Mum 92 31
## 279 English F Mum 82 31
## 280 English S Mum 72 41
## 281 English F Mum 74 71
## 282 English S Mum 74 60
## 283 English F Mum 95 94
## 284 English S Mum 97 87
## 285 English F Father 40 38
## 286 English S Father 41 39
## 287 English F Mum 51 79
## 288 English S Mum 81 79
## 289 English F Mum 71 70
## 290 English S Mum 75 71
## 291 Science F Father 49 36
## 292 Science S Father 42 24
## 293 Science F Mum 90 86
## 294 Science S Mum 62 64
## 295 Science F Father 90 86
## 296 Science S Father 62 64
## 297 Science F Mum 98 87
## 298 Science S Mum 72 74
## 299 Science F Father 18 17
## 300 Science S Father 32 14
## 301 Science F Father 10 12
## 302 Science S Father 22 34
## 303 Science F Mum 11 20
## 304 Science S Mum 12 44
## 305 Science F Father 15 50
## 306 Science S Father 32 44
## 307 Science F Mum 65 59
## 308 Science S Mum 72 64
## 309 Science F Mum 95 57
## 310 Science S Mum 82 84
## 311 Science F Father 25 29
## 312 Science S Father 42 34
## 313 Science F Mum 55 79
## 314 Science S Mum 62 64
## 315 Science F Mum 78 88
## 316 Science S Mum 72 84
## 317 French F Mum 60 92
## 318 French S Mum 50 90
## 319 French F Mum 60 97
## 320 French S Mum 50 95
## 321 French F Mum 60 87
## 322 French S Mum 50 80
## 323 French F Father 10 15
## 324 French S Father 30 10
## 325 French F Father 24 35
## 326 French S Father 27 20
## 327 French F Father 10 15
## 328 French S Father 30 10
## 329 French F Mum 80 77
## 330 French S Mum 75 85
## 331 French F Father 40 7
## 332 French S Father 35 2
## 333 French F Mum 10 90
## 334 French S Mum 25 80
## 335 French F Mum 10 8
## 336 French S Mum 15 7
## 337 French F Mum 70 90
## 338 French S Mum 75 80
## 339 French F Father 78 98
## 340 French S Father 79 89
## 341 French F Father 16 90
## 342 French S Father 17 80
## 343 French F Mum 40 87
## 344 French S Mum 35 92
## 345 French F Mum 14 97
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## 398 Biology S Father 85 82
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## 400 Biology S Father 89 92
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## 407 Biology F Mum 2 9
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## 413 Biology F Father 78 80
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## 415 Biology F Mum 10 9
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## 417 Biology F Father 98 90
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## 419 Biology F Father 88 90
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## 422 Chemistry S Mum 84 92
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## 424 Geology S Mum 74 62
## 425 Geology F Mum 90 79
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## 444 History S Mum 70 76
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## 446 Chemistry S Mum 72 76
## 447 Geology F Mum 77 82
## 448 Geology S Mum 71 84
## 449 History F Mum 87 92
## 450 History S Mum 81 84
## 451 Chemistry F Father 15 97
## 452 Chemistry S Father 19 98
## 453 Geology F Father 25 82
## 454 Geology S Father 29 78
## 455 History F Father 45 87
## 456 History S Father 39 88
## 457 Chemistry F Father 85 90
## 458 Chemistry S Father 79 91
## 459 Chemistry F Father 90 91
## 460 Chemistry S Father 80 90
## 461 Geology F Father 80 83
## 462 Geology S Father 71 81
## 463 History F Father 72 87
## 464 History S Father 69 77
## 465 Chemistry F Mum 85 99
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## 467 Geology F Mum 80 82
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## 470 Chemistry S Father 9 6
## 471 History F Father 81 86
## 472 History S Father 78 82
## 473 Geology F Father 80 87
## 474 Geology S Father 85 88
## 475 Chemistry F Father 2 7
## 476 Chemistry S Father 5 4
## 477 Geology F Father 50 77
## 478 Geology S Father 55 74
## 479 History F Father 30 17
## 480 History S Father 35 14
## AnnouncementsView Discussion ParentAnsweringSurvey
## 1 2 20 Yes
## 2 3 25 Yes
## 3 0 30 No
## 4 5 35 No
## 5 12 50 No
## 6 13 70 Yes
## 7 0 17 No
## 8 15 22 Yes
## 9 16 50 Yes
## 10 25 70 Yes
## 11 30 80 Yes
## 12 19 12 Yes
## 13 0 11 No
## 14 12 19 No
## 15 44 60 No
## 16 22 66 Yes
## 17 20 80 No
## 18 35 90 No
## 19 36 96 Yes
## 20 40 99 Yes
## 21 33 90 No
## 22 4 80 No
## 23 2 90 No
## 24 2 50 No
## 25 3 70 Yes
## 26 30 40 Yes
## 27 25 40 Yes
## 28 12 33 No
## 29 52 43 Yes
## 30 33 35 No
## 31 50 70 Yes
## 32 40 16 Yes
## 33 10 50 Yes
## 34 9 40 No
## 35 8 40 No
## 36 17 30 No
## 37 4 22 Yes
## 38 26 90 Yes
## 39 0 4 No
## 40 37 70 Yes
## 41 3 70 No
## 42 16 50 No
## 43 7 33 Yes
## 44 28 90 Yes
## 45 30 90 No
## 46 15 70 No
## 47 1 30 No
## 48 39 90 Yes
## 49 40 88 Yes
## 50 50 77 Yes
## 51 12 70 No
## 52 16 50 No
## 53 16 40 No
## 54 19 75 Yes
## 55 8 30 No
## 56 6 20 Yes
## 57 4 1 Yes
## 58 6 5 Yes
## 59 19 50 Yes
## 60 22 40 No
## 61 28 20 No
## 62 32 40 Yes
## 63 70 80 Yes
## 64 16 12 Yes
## 65 7 2 No
## 66 18 19 No
## 67 19 30 No
## 68 23 80 Yes
## 69 35 30 Yes
## 70 11 33 No
## 71 10 12 Yes
## 72 9 20 Yes
## 73 2 8 No
## 74 26 19 Yes
## 75 6 13 No
## 76 29 60 Yes
## 77 37 44 Yes
## 78 33 33 Yes
## 79 11 9 No
## 80 49 55 Yes
## 81 10 40 Yes
## 82 1 70 No
## 83 2 80 Yes
## 84 10 50 No
## 85 50 10 Yes
## 86 1 12 No
## 87 40 50 Yes
## 88 3 50 No
## 89 4 33 No
## 90 1 50 Yes
## 91 10 60 Yes
## 92 55 19 Yes
## 93 19 15 Yes
## 94 40 40 Yes
## 95 35 10 No
## 96 2 70 No
## 97 11 75 Yes
## 98 38 12 No
## 99 0 30 No
## 100 9 50 Yes
## 101 40 90 Yes
## 102 50 7 Yes
## 103 2 8 No
## 104 6 10 No
## 105 3 3 Yes
## 106 5 80 Yes
## 107 7 40 Yes
## 108 41 33 Yes
## 109 5 3 No
## 110 30 14 No
## 111 55 40 Yes
## 112 51 80 No
## 113 3 50 No
## 114 0 69 No
## 115 0 50 No
## 116 11 70 Yes
## 117 12 19 Yes
## 118 40 17 No
## 119 23 22 Yes
## 120 3 60 No
## 121 0 50 Yes
## 122 8 30 No
## 123 55 12 Yes
## 124 7 5 Yes
## 125 4 15 Yes
## 126 0 17 No
## 127 7 55 Yes
## 128 6 9 No
## 129 20 16 No
## 130 2 2 Yes
## 131 5 30 No
## 132 20 50 Yes
## 133 10 3 No
## 134 0 70 Yes
## 135 15 70 Yes
## 136 95 70 Yes
## 137 95 70 Yes
## 138 50 40 No
## 139 95 90 No
## 140 85 70 No
## 141 5 20 Yes
## 142 50 10 No
## 143 55 11 Yes
## 144 55 19 Yes
## 145 15 19 No
## 146 55 11 Yes
## 147 50 70 Yes
## 148 30 30 No
## 149 30 40 No
## 150 50 70 Yes
## 151 51 59 Yes
## 152 71 89 Yes
## 153 98 40 Yes
## 154 40 40 No
## 155 33 41 No
## 156 73 91 Yes
## 157 30 49 Yes
## 158 33 40 No
## 159 48 10 No
## 160 48 80 Yes
## 161 58 50 Yes
## 162 58 66 No
## 163 3 73 No
## 164 93 43 No
## 165 83 14 Yes
## 166 73 66 Yes
## 167 53 43 No
## 168 83 43 Yes
## 169 23 33 Yes
## 170 29 23 No
## 171 83 33 Yes
## 172 23 33 No
## 173 53 13 No
## 174 23 33 Yes
## 175 73 43 Yes
## 176 13 53 No
## 177 23 33 Yes
## 178 83 11 Yes
## 179 33 13 Yes
## 180 20 33 No
## 181 63 93 Yes
## 182 13 33 No
## 183 62 53 Yes
## 184 32 53 No
## 185 30 13 No
## 186 82 33 Yes
## 187 22 53 Yes
## 188 52 51 Yes
## 189 72 53 Yes
## 190 12 23 No
## 191 32 53 No
## 192 37 13 Yes
## 193 62 53 Yes
## 194 62 53 No
## 195 72 23 No
## 196 42 33 Yes
## 197 62 53 Yes
## 198 62 53 No
## 199 42 53 No
## 200 12 13 No
## 201 52 83 Yes
## 202 52 43 Yes
## 203 12 33 Yes
## 204 42 40 Yes
## 205 12 24 No
## 206 42 24 Yes
## 207 42 14 Yes
## 208 42 14 Yes
## 209 49 14 No
## 210 39 84 No
## 211 82 14 Yes
## 212 42 14 No
## 213 42 19 Yes
## 214 22 14 No
## 215 2 4 No
## 216 42 14 No
## 217 32 61 Yes
## 218 40 10 Yes
## 219 42 94 Yes
## 220 46 34 Yes
## 221 41 13 Yes
## 222 40 24 No
## 223 82 40 Yes
## 224 12 90 Yes
## 225 22 70 Yes
## 226 12 30 No
## 227 62 30 No
## 228 2 10 Yes
## 229 77 81 Yes
## 230 21 10 No
## 231 12 29 No
## 232 21 20 Yes
## 233 15 50 Yes
## 234 58 46 Yes
## 235 58 66 Yes
## 236 18 26 No
## 237 13 6 No
## 238 58 66 Yes
## 239 58 66 Yes
## 240 88 76 Yes
## 241 98 96 Yes
## 242 58 86 Yes
## 243 18 6 No
## 244 38 46 No
## 245 83 85 Yes
## 246 52 66 Yes
## 247 62 77 Yes
## 248 82 96 No
## 249 2 16 No
## 250 62 40 No
## 251 32 40 Yes
## 252 32 17 No
## 253 82 94 No
## 254 12 44 No
## 255 42 70 Yes
## 256 12 40 Yes
## 257 72 60 No
## 258 66 65 No
## 259 86 95 Yes
## 260 12 17 No
## 261 12 14 No
## 262 82 64 Yes
## 263 52 34 Yes
## 264 42 7 No
## 265 42 32 No
## 266 82 37 No
## 267 12 17 Yes
## 268 32 29 Yes
## 269 10 10 Yes
## 270 40 20 Yes
## 271 52 30 No
## 272 32 36 No
## 273 2 16 No
## 274 6 26 No
## 275 82 70 Yes
## 276 80 60 Yes
## 277 22 26 Yes
## 278 42 27 Yes
## 279 52 25 No
## 280 46 27 No
## 281 56 37 No
## 282 56 37 No
## 283 72 80 No
## 284 82 86 No
## 285 32 36 No
## 286 35 39 Yes
## 287 85 89 Yes
## 288 91 85 Yes
## 289 65 69 Yes
## 290 76 80 Yes
## 291 32 44 No
## 292 12 24 No
## 293 82 74 Yes
## 294 72 84 Yes
## 295 82 74 Yes
## 296 72 84 Yes
## 297 86 84 No
## 298 82 89 No
## 299 26 34 No
## 300 32 29 No
## 301 20 13 No
## 302 15 9 No
## 303 21 23 No
## 304 25 39 No
## 305 24 43 Yes
## 306 29 89 Yes
## 307 74 83 Yes
## 308 59 89 Yes
## 309 74 89 Yes
## 310 79 79 Yes
## 311 34 33 No
## 312 29 39 No
## 313 44 43 Yes
## 314 69 49 Yes
## 315 74 83 Yes
## 316 89 89 Yes
## 317 83 23 Yes
## 318 83 13 Yes
## 319 13 3 Yes
## 320 23 10 Yes
## 321 23 11 Yes
## 322 20 15 Yes
## 323 10 21 No
## 324 20 5 No
## 325 18 31 No
## 326 33 35 No
## 327 10 21 No
## 328 20 5 No
## 329 63 83 Yes
## 330 73 80 Yes
## 331 50 41 No
## 332 29 33 No
## 333 30 21 No
## 334 29 23 No
## 335 9 3 No
## 336 12 7 No
## 337 20 21 No
## 338 15 20 No
## 339 10 11 No
## 340 11 14 No
## 341 15 11 No
## 342 13 10 No
## 343 50 41 No
## 344 29 33 No
## 345 15 14 No
## 346 20 30 No
## 347 15 14 No
## 348 20 30 No
## 349 9 3 No
## 350 12 7 No
## 351 6 6 No
## 352 11 10 No
## 353 40 11 Yes
## 354 21 13 Yes
## 355 42 21 Yes
## 356 41 23 Yes
## 357 32 25 Yes
## 358 21 27 Yes
## 359 52 15 Yes
## 360 51 17 Yes
## 361 31 28 Yes
## 362 41 38 Yes
## 363 41 38 Yes
## 364 51 39 Yes
## 365 21 28 Yes
## 366 31 29 Yes
## 367 71 38 Yes
## 368 51 48 Yes
## 369 60 31 No
## 370 51 34 No
## 371 50 21 No
## 372 29 23 No
## 373 22 97 No
## 374 19 98 No
## 375 40 51 No
## 376 29 23 No
## 377 50 61 Yes
## 378 59 63 Yes
## 379 50 91 Yes
## 380 59 83 Yes
## 381 20 98 No
## 382 29 93 No
## 383 20 28 Yes
## 384 29 33 Yes
## 385 40 28 Yes
## 386 49 23 Yes
## 387 21 97 Yes
## 388 31 88 Yes
## 389 11 7 No
## 390 1 8 No
## 391 50 10 Yes
## 392 54 12 Yes
## 393 50 20 Yes
## 394 57 22 Yes
## 395 50 40 Yes
## 396 54 72 Yes
## 397 46 61 Yes
## 398 59 63 Yes
## 399 86 81 Yes
## 400 89 83 Yes
## 401 40 26 No
## 402 34 22 No
## 403 80 86 No
## 404 64 82 No
## 405 70 70 Yes
## 406 87 72 Yes
## 407 10 1 No
## 408 19 3 No
## 409 10 31 No
## 410 9 33 No
## 411 12 21 No
## 412 19 43 No
## 413 66 51 Yes
## 414 79 83 Yes
## 415 2 1 No
## 416 9 3 No
## 417 86 71 Yes
## 418 89 83 Yes
## 419 76 81 Yes
## 420 89 84 Yes
## 421 22 31 Yes
## 422 29 43 Yes
## 423 46 45 Yes
## 424 49 43 Yes
## 425 76 79 Yes
## 426 79 68 Yes
## 427 62 11 Yes
## 428 69 13 Yes
## 429 2 41 No
## 430 9 49 No
## 431 22 61 No
## 432 23 68 No
## 433 62 81 No
## 434 73 98 No
## 435 72 10 Yes
## 436 79 20 Yes
## 437 71 30 Yes
## 438 70 23 Yes
## 439 76 19 Yes
## 440 75 29 Yes
## 441 86 15 Yes
## 442 85 10 Yes
## 443 76 75 Yes
## 444 65 70 Yes
## 445 64 39 Yes
## 446 66 40 Yes
## 447 74 79 Yes
## 448 67 80 Yes
## 449 74 89 Yes
## 450 77 85 Yes
## 451 22 10 Yes
## 452 20 2 Yes
## 453 52 20 Yes
## 454 40 12 Yes
## 455 57 80 Yes
## 456 43 72 Yes
## 457 52 30 Yes
## 458 49 22 Yes
## 459 80 81 Yes
## 460 80 83 Yes
## 461 82 71 Yes
## 462 83 72 Yes
## 463 72 89 Yes
## 464 83 92 Yes
## 465 42 90 Yes
## 466 45 92 Yes
## 467 64 58 Yes
## 468 63 60 Yes
## 469 14 80 No
## 470 15 85 No
## 471 86 41 Yes
## 472 78 53 Yes
## 473 74 68 Yes
## 474 79 70 Yes
## 475 4 8 No
## 476 5 8 No
## 477 14 28 No
## 478 25 29 No
## 479 14 57 No
## 480 23 62 No
## ParentschoolSatisfaction StudentAbsenceDays Class
## 1 Good Under-7 M
## 2 Good Under-7 M
## 3 Bad Above-7 L
## 4 Bad Above-7 L
## 5 Bad Above-7 M
## 6 Bad Above-7 M
## 7 Bad Above-7 L
## 8 Good Under-7 M
## 9 Good Under-7 M
## 10 Good Under-7 M
## 11 Good Under-7 H
## 12 Good Under-7 M
## 13 Bad Above-7 L
## 14 Bad Above-7 L
## 15 Bad Above-7 H
## 16 Good Under-7 M
## 17 Bad Above-7 M
## 18 Bad Above-7 M
## 19 Good Under-7 M
## 20 Good Under-7 H
## 21 Bad Above-7 M
## 22 Bad Under-7 M
## 23 Bad Under-7 M
## 24 Bad Above-7 L
## 25 Good Above-7 L
## 26 Good Above-7 L
## 27 Bad Under-7 M
## 28 Bad Above-7 L
## 29 Good Under-7 M
## 30 Bad Under-7 M
## 31 Good Under-7 H
## 32 Good Above-7 L
## 33 Good Above-7 L
## 34 Bad Above-7 L
## 35 Bad Above-7 L
## 36 Bad Above-7 L
## 37 Good Above-7 L
## 38 Good Under-7 M
## 39 Bad Above-7 L
## 40 Good Under-7 M
## 41 Bad Above-7 L
## 42 Good Under-7 M
## 43 Good Above-7 L
## 44 Good Under-7 M
## 45 Bad Under-7 M
## 46 Good Above-7 L
## 47 Bad Above-7 L
## 48 Good Under-7 H
## 49 Good Under-7 H
## 50 Good Under-7 M
## 51 Bad Above-7 L
## 52 Good Above-7 L
## 53 Bad Under-7 M
## 54 Good Under-7 H
## 55 Bad Above-7 L
## 56 Good Above-7 L
## 57 Good Above-7 L
## 58 Good Above-7 L
## 59 Good Under-7 M
## 60 Good Under-7 M
## 61 Good Above-7 L
## 62 Good Under-7 M
## 63 Good Under-7 H
## 64 Bad Under-7 M
## 65 Bad Above-7 L
## 66 Bad Above-7 L
## 67 Bad Above-7 M
## 68 Good Under-7 H
## 69 Good Under-7 H
## 70 Bad Under-7 M
## 71 Good Above-7 L
## 72 Good Under-7 M
## 73 Bad Under-7 L
## 74 Bad Above-7 M
## 75 Bad Under-7 L
## 76 Good Under-7 M
## 77 Good Under-7 M
## 78 Good Under-7 M
## 79 Bad Above-7 L
## 80 Bad Under-7 H
## 81 Bad Under-7 L
## 82 Bad Above-7 L
## 83 Good Above-7 L
## 84 Good Above-7 M
## 85 Good Above-7 H
## 86 Bad Above-7 L
## 87 Good Under-7 H
## 88 Bad Under-7 L
## 89 Bad Under-7 L
## 90 Good Above-7 L
## 91 Good Above-7 L
## 92 Good Under-7 H
## 93 Good Under-7 H
## 94 Good Under-7 H
## 95 Good Above-7 L
## 96 Bad Under-7 H
## 97 Good Under-7 H
## 98 Bad Under-7 M
## 99 Bad Under-7 M
## 100 Bad Above-7 M
## 101 Bad Under-7 M
## 102 Good Under-7 H
## 103 Bad Above-7 L
## 104 Bad Above-7 L
## 105 Good Above-7 M
## 106 Good Above-7 L
## 107 Good Under-7 M
## 108 Bad Under-7 H
## 109 Bad Above-7 M
## 110 Bad Under-7 M
## 111 Good Under-7 H
## 112 Good Under-7 M
## 113 Bad Above-7 L
## 114 Bad Above-7 L
## 115 Bad Above-7 L
## 116 Good Above-7 L
## 117 Good Above-7 M
## 118 Good Under-7 H
## 119 Good Above-7 M
## 120 Bad Under-7 M
## 121 Good Under-7 M
## 122 Bad Above-7 L
## 123 Good Above-7 M
## 124 Good Under-7 M
## 125 Bad Under-7 L
## 126 Good Above-7 L
## 127 Good Under-7 M
## 128 Bad Above-7 L
## 129 Bad Under-7 L
## 130 Good Under-7 L
## 131 Bad Above-7 L
## 132 Good Under-7 M
## 133 Bad Above-7 L
## 134 Good Above-7 L
## 135 Good Under-7 H
## 136 Good Under-7 H
## 137 Good Under-7 H
## 138 Bad Under-7 M
## 139 Bad Under-7 H
## 140 Bad Under-7 M
## 141 Bad Above-7 L
## 142 Bad Above-7 L
## 143 Good Under-7 M
## 144 Good Under-7 H
## 145 Bad Above-7 L
## 146 Bad Under-7 M
## 147 Good Under-7 H
## 148 Bad Under-7 M
## 149 Bad Under-7 M
## 150 Bad Under-7 H
## 151 Good Under-7 H
## 152 Good Under-7 M
## 153 Bad Under-7 H
## 154 Bad Above-7 L
## 155 Bad Above-7 M
## 156 Bad Under-7 H
## 157 Good Under-7 M
## 158 Good Under-7 M
## 159 Bad Above-7 L
## 160 Good Under-7 M
## 161 Good Under-7 H
## 162 Bad Under-7 M
## 163 Bad Under-7 H
## 164 Bad Above-7 M
## 165 Good Above-7 M
## 166 Good Under-7 H
## 167 Bad Under-7 M
## 168 Good Under-7 H
## 169 Good Under-7 H
## 170 Bad Under-7 M
## 171 Good Under-7 H
## 172 Good Under-7 M
## 173 Good Above-7 L
## 174 Good Above-7 L
## 175 Bad Above-7 M
## 176 Bad Above-7 L
## 177 Bad Under-7 H
## 178 Bad Under-7 M
## 179 Bad Under-7 H
## 180 Bad Under-7 M
## 181 Bad Under-7 H
## 182 Bad Above-7 L
## 183 Bad Under-7 H
## 184 Bad Under-7 M
## 185 Bad Above-7 L
## 186 Good Under-7 H
## 187 Good Under-7 M
## 188 Good Under-7 M
## 189 Good Under-7 H
## 190 Bad Under-7 M
## 191 Bad Under-7 L
## 192 Good Above-7 L
## 193 Good Under-7 M
## 194 Bad Under-7 M
## 195 Bad Under-7 M
## 196 Good Under-7 M
## 197 Good Under-7 H
## 198 Bad Under-7 H
## 199 Bad Above-7 L
## 200 Bad Under-7 M
## 201 Bad Under-7 H
## 202 Good Under-7 H
## 203 Good Under-7 M
## 204 Bad Under-7 M
## 205 Bad Above-7 L
## 206 Bad Above-7 H
## 207 Good Under-7 M
## 208 Good Under-7 M
## 209 Bad Under-7 M
## 210 Bad Under-7 M
## 211 Good Under-7 H
## 212 Bad Under-7 M
## 213 Good Under-7 H
## 214 Bad Above-7 L
## 215 Bad Above-7 L
## 216 Good Above-7 L
## 217 Bad Above-7 M
## 218 Good Under-7 M
## 219 Bad Under-7 H
## 220 Good Under-7 M
## 221 Good Under-7 M
## 222 Good Under-7 M
## 223 Bad Under-7 M
## 224 Good Under-7 H
## 225 Bad Under-7 H
## 226 Bad Under-7 M
## 227 Bad Under-7 L
## 228 Good Under-7 L
## 229 Good Above-7 H
## 230 Bad Above-7 L
## 231 Bad Above-7 M
## 232 Good Above-7 L
## 233 Good Above-7 M
## 234 Good Above-7 M
## 235 Good Above-7 M
## 236 Bad Above-7 L
## 237 Bad Above-7 L
## 238 Good Above-7 M
## 239 Good Above-7 M
## 240 Good Under-7 H
## 241 Bad Under-7 H
## 242 Good Under-7 M
## 243 Bad Above-7 L
## 244 Bad Above-7 M
## 245 Bad Under-7 H
## 246 Bad Under-7 M
## 247 Good Under-7 H
## 248 Bad Above-7 M
## 249 Bad Above-7 L
## 250 Good Above-7 M
## 251 Good Under-7 H
## 252 Good Above-7 L
## 253 Bad Under-7 M
## 254 Bad Above-7 L
## 255 Good Under-7 H
## 256 Good Under-7 H
## 257 Good Under-7 H
## 258 Good Under-7 M
## 259 Good Under-7 M
## 260 Bad Above-7 L
## 261 Bad Above-7 L
## 262 Bad Above-7 M
## 263 Good Above-7 M
## 264 Bad Above-7 M
## 265 Bad Under-7 M
## 266 Bad Under-7 H
## 267 Good Above-7 M
## 268 Good Above-7 M
## 269 Bad Above-7 M
## 270 Bad Above-7 M
## 271 Good Under-7 H
## 272 Good Under-7 M
## 273 Bad Under-7 M
## 274 Bad Under-7 H
## 275 Good Under-7 H
## 276 Good Under-7 H
## 277 Good Under-7 H
## 278 Good Under-7 H
## 279 Good Under-7 M
## 280 Good Under-7 M
## 281 Good Under-7 H
## 282 Good Under-7 H
## 283 Good Under-7 H
## 284 Good Under-7 H
## 285 Bad Above-7 M
## 286 Bad Above-7 M
## 287 Good Under-7 H
## 288 Good Under-7 H
## 289 Good Under-7 M
## 290 Good Under-7 M
## 291 Bad Above-7 L
## 292 Bad Above-7 L
## 293 Good Under-7 H
## 294 Good Under-7 H
## 295 Good Above-7 M
## 296 Good Above-7 M
## 297 Good Under-7 H
## 298 Good Under-7 H
## 299 Good Above-7 M
## 300 Good Above-7 M
## 301 Bad Above-7 L
## 302 Bad Above-7 L
## 303 Bad Under-7 M
## 304 Bad Under-7 M
## 305 Bad Under-7 M
## 306 Bad Under-7 M
## 307 Good Under-7 H
## 308 Good Under-7 H
## 309 Good Above-7 M
## 310 Good Above-7 M
## 311 Good Above-7 M
## 312 Good Above-7 M
## 313 Good Under-7 H
## 314 Good Under-7 H
## 315 Good Under-7 H
## 316 Good Under-7 H
## 317 Good Under-7 H
## 318 Good Under-7 H
## 319 Good Under-7 H
## 320 Good Under-7 H
## 321 Good Above-7 M
## 322 Good Above-7 M
## 323 Bad Above-7 L
## 324 Bad Above-7 L
## 325 Bad Under-7 M
## 326 Bad Under-7 M
## 327 Bad Above-7 L
## 328 Bad Above-7 L
## 329 Good Under-7 M
## 330 Good Under-7 M
## 331 Good Above-7 L
## 332 Good Above-7 L
## 333 Good Under-7 M
## 334 Good Under-7 M
## 335 Good Above-7 L
## 336 Good Above-7 L
## 337 Good Under-7 M
## 338 Good Under-7 M
## 339 Good Under-7 M
## 340 Good Under-7 M
## 341 Good Under-7 M
## 342 Good Under-7 M
## 343 Bad Under-7 M
## 344 Bad Under-7 M
## 345 Good Under-7 H
## 346 Good Under-7 H
## 347 Good Under-7 H
## 348 Good Under-7 H
## 349 Good Above-7 L
## 350 Good Above-7 L
## 351 Good Above-7 L
## 352 Good Above-7 L
## 353 Good Under-7 M
## 354 Good Under-7 M
## 355 Good Under-7 H
## 356 Good Under-7 H
## 357 Good Under-7 M
## 358 Good Under-7 M
## 359 Good Under-7 H
## 360 Good Under-7 H
## 361 Good Above-7 M
## 362 Good Above-7 M
## 363 Good Under-7 H
## 364 Good Under-7 H
## 365 Good Under-7 H
## 366 Good Under-7 H
## 367 Good Above-7 M
## 368 Good Above-7 M
## 369 Good Under-7 H
## 370 Good Under-7 H
## 371 Bad Under-7 M
## 372 Bad Under-7 M
## 373 Bad Above-7 L
## 374 Bad Above-7 L
## 375 Bad Above-7 L
## 376 Bad Above-7 L
## 377 Bad Above-7 M
## 378 Bad Above-7 M
## 379 Bad Above-7 L
## 380 Bad Above-7 L
## 381 Bad Above-7 L
## 382 Bad Above-7 L
## 383 Good Under-7 H
## 384 Good Under-7 H
## 385 Good Under-7 H
## 386 Good Under-7 H
## 387 Good Under-7 M
## 388 Good Under-7 M
## 389 Good Above-7 L
## 390 Good Above-7 L
## 391 Good Above-7 M
## 392 Good Above-7 M
## 393 Good Under-7 H
## 394 Good Under-7 H
## 395 Good Under-7 H
## 396 Good Under-7 H
## 397 Bad Under-7 M
## 398 Bad Under-7 M
## 399 Good Under-7 H
## 400 Good Under-7 H
## 401 Good Above-7 M
## 402 Good Above-7 M
## 403 Good Under-7 H
## 404 Good Under-7 H
## 405 Good Under-7 H
## 406 Good Under-7 H
## 407 Bad Above-7 L
## 408 Bad Above-7 L
## 409 Bad Under-7 M
## 410 Bad Under-7 M
## 411 Good Under-7 H
## 412 Good Under-7 H
## 413 Good Under-7 M
## 414 Good Under-7 M
## 415 Good Above-7 L
## 416 Good Above-7 L
## 417 Good Under-7 H
## 418 Good Under-7 H
## 419 Good Under-7 H
## 420 Good Under-7 H
## 421 Good Under-7 H
## 422 Good Under-7 H
## 423 Good Above-7 M
## 424 Good Above-7 M
## 425 Good Under-7 H
## 426 Good Under-7 H
## 427 Good Above-7 M
## 428 Good Above-7 M
## 429 Bad Above-7 L
## 430 Bad Above-7 L
## 431 Bad Under-7 H
## 432 Bad Under-7 H
## 433 Bad Under-7 M
## 434 Bad Under-7 M
## 435 Good Above-7 M
## 436 Good Above-7 M
## 437 Good Under-7 M
## 438 Good Under-7 M
## 439 Good Under-7 M
## 440 Good Under-7 M
## 441 Good Under-7 M
## 442 Good Under-7 M
## 443 Good Above-7 M
## 444 Good Above-7 M
## 445 Good Above-7 L
## 446 Good Above-7 L
## 447 Good Under-7 M
## 448 Good Under-7 M
## 449 Good Under-7 H
## 450 Good Under-7 H
## 451 Good Under-7 H
## 452 Good Under-7 H
## 453 Good Above-7 M
## 454 Good Above-7 M
## 455 Good Under-7 M
## 456 Good Under-7 M
## 457 Good Under-7 H
## 458 Good Under-7 H
## 459 Good Under-7 H
## 460 Good Under-7 H
## 461 Good Above-7 M
## 462 Good Above-7 M
## 463 Good Above-7 M
## 464 Good Above-7 M
## 465 Good Under-7 H
## 466 Good Under-7 H
## 467 Good Under-7 H
## 468 Good Under-7 H
## 469 Bad Above-7 L
## 470 Bad Above-7 L
## 471 Good Under-7 M
## 472 Good Under-7 M
## 473 Good Under-7 M
## 474 Good Under-7 M
## 475 Bad Above-7 L
## 476 Bad Above-7 L
## 477 Bad Under-7 M
## 478 Bad Under-7 M
## 479 Bad Above-7 L
## 480 Bad Above-7 LTask 1.2. Bar plots of classification values distribution:
Gender distribution:
ggplot(data = E, aes(x = gender, fill = Relation)) + geom_bar() +
labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Gender Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,320,20))
There are nearly 175 girls and 305 boys in this data set. The number of girls who have mom as guardian nearly the same as having father as guardian, whereas most boys have father as guardians.
ggplot(data = E, aes(x = gender, fill = ParentAnsweringSurvey)) + geom_bar() +
labs(x = "Gender", y = "Student Count") + ggtitle(label = "Gender Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(gender, ParentAnsweringSurvey) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(gender) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))## Warning: package 'bindrcpp' was built under R version 3.4.2 ggplot(ESummary, aes(x = gender, y = count)) +
geom_bar(aes(fill = ParentAnsweringSurvey), stat ="identity", position = 'fill')+ labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Normalized Gender Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=0, hjust=1))+ scale_y_continuous(breaks = seq(0,1,0.2))
The rates of parent answering survey are nearly the same between girls and boys, which are both under 60%.
ggplot(data = E, aes(x = gender, fill = ParentschoolSatisfaction)) + geom_bar() +
labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Gender Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(gender, ParentschoolSatisfaction) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(gender) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = gender, y = count)) +
geom_bar(aes(fill = ParentschoolSatisfaction), stat ="identity", position = 'fill')+ labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Normalized Gender Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.1))
Girls’ parents has higher school satisfaction than boys. Girls have higher rate(with about 67%) of parent school satisfaction in good than boys(with about 57%).
ggplot(data = E, aes(x = gender, fill = StudentAbsenceDays)) + geom_bar() +
labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Gender Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(gender, StudentAbsenceDays) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(gender) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = gender, y = count)) +
geom_bar(aes(fill = StudentAbsenceDays), stat ="identity", position = 'fill')+ labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Normalized Gender Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.1))
Boys has higher rate of above 7 absence days.
ggplot(data = E, aes(x = gender, fill = Class)) + geom_bar() +
labs(x = "Gender", y = "Student Count") + ggtitle(label = "Gender Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(gender, Class) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(gender) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = gender, y = count)) +
geom_bar(aes(fill = Class), stat ="identity", position = 'fill')+ labs(x = "Gender", y = "Student Count")+ ggtitle(label = "Normalized Gender Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.1))
Girls have higher rate (over 40%) of people with high academic performance, whereas a small rate of boys (about 20%) have high performance. The rate of median class is almost the same both for girls and boys, which are both about 40%. About 35% rate of boys have low performance, whereas the rate of low class is very low among girls.
Place of Birth distribution:
ggplot(data = E, aes(x = PlaceofBirth, fill = Class)) + geom_bar() +
labs(x = "PlaceofBirth", y = "Student Count") + ggtitle(label = "PlaceofBirth Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))+ scale_y_continuous(breaks = seq(0,200,10))
ESummary = E %>%
# Get the counts
group_by(PlaceofBirth, Class) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(PlaceofBirth) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = PlaceofBirth, y = count)) +
geom_bar(aes(fill = Class), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized PlaceofBirth Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
Most of the students were born in Jordan and Kuwait. Venzuela has only one student.
Iraq, Lebanon, Saudiarabia, Palestine, USA and Venzuela have higher rate of student with high class, whereas Kuwait and Lybia have higher rate of students with low class.
ggplot(data = E, aes(x = PlaceofBirth, fill = NationalITy)) + geom_bar(colour= "Black") +
labs(x = "PlaceofBirth", y = "Student Count")+ ggtitle(label = "PlaceofBirth Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold")) +theme(axis.text.x = element_text(angle=90, hjust=1))
Most of the students who were born in these countries of Egypt, Iran, Iraq, Kuwait, Lebanon, Lybia, Morocco, Syria, Tunis and Venzuela have the same nationality as the place of birth. some of the students who were born in Jordan have the nationality of Palestine. The students who were born in USA have diversity of nationality. Half of the students who were born in Saudiarabia have the nationality of Jordan.
ggplot(data = E, aes(x = PlaceofBirth, fill = gender)) + geom_bar() +
labs(x = "PlaceofBirth", y = "Student Count")+ ggtitle(label = "PlaceofBirth Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold")) +theme(axis.text.x = element_text(angle=90, hjust=1))
Lebanon has most rate of girls, half of the USA students are girls. Girls are less than boys in the other areas.
ggplot(data = E, aes(x = PlaceofBirth, fill = Relation)) + geom_bar() +
labs(x = "PlaceofBirth", y = "Student Count")+ ggtitle(label = "PlaceofBirth Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold")) +theme(axis.text.x = element_text(angle=90, hjust=1))
Based on the place of birth, most fathers are responsible for students in some countries like: Kuwait, Iraq, Saudiarabia. Most mothers are responsible for students’ study in these areas like: Egypt, Iran, Jordan, Lebanon, LYbia, Morocco, Palestine and Venzuela.
Nationality distribution:
ggplot(data = E, aes(x = NationalITy, fill = Class)) + geom_bar() +
labs(x = "Nationality", y = "Student Count")+ ggtitle(label = "Nationality Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))+ scale_y_continuous(breaks = seq(0,200,10))
Jordan and KW(KuwaIT) has highest number of students. Venzuela has only one student.
ESummary = E %>%
# Get the counts
group_by(NationalITy, Class) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(NationalITy) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = NationalITy, y = count)) +
geom_bar(aes(fill = Class), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized NationalITy Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
Iraq, Lebanon, Saudiarabia, Palestine, USA and Venzuela have higher rate of student with high class, whereas Kuwait and Lybia have higher rate of students with low class.
ggplot(data = E, aes(x = NationalITy, fill = Relation)) + geom_bar() +
labs(x = "PlaceofBirth", y = "Student Count")+ ggtitle(label = "Nationality Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold")) +theme(axis.text.x = element_text(angle=90, hjust=1))
Based on the nationality,most fathers are responsible for students in some countries like: Kuwait, Iraq, Saudiarabia. Most mothers are responsible for students’ study in these areas like: Egypt, Iran, Jordan, Lebanon, LYbia, Morocco, Palestine and Venzuela.
ggplot(data = E, aes(x = NationalITy, fill = ParentAnsweringSurvey)) + geom_bar() +
labs(x = "NationalITy", y = "Student Count") + ggtitle(label = "Nationality Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
Most of the areas have higher rate of parent answering survey, except Lybia.
ggplot(data = E, aes(x = NationalITy, fill = ParentschoolSatisfaction)) + geom_bar() +
labs(x = "NationalITy", y = "Student Count")+ ggtitle(label = "Nationality Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
Most of the areas have higher rate of good parent school satisfaction, except Suadiarabia.
Stage ID distribution:
ggplot(data = E, aes(x = StageID, fill = gender)) + geom_bar() +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Middle school has the highest number of students. High school has very few students. Girls are less than boys in every level of school.
ggplot(data = E, aes(x = StageID, fill = Topic)) + geom_bar(colour = "black") +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Lowerlevel has less kinds of topics. Middle school almost covers all kinds of topics.
ggplot(data = E, aes(x = StageID, fill = Semester)) + geom_bar() +
labs(x = "StageID", y = "Student Count")+ ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
The two semesters almost cover the same rate in different level of schools.
ggplot(data = E, aes(x = StageID, fill = Relation)) + geom_bar() +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
More fatheres are responsible for students’ study in different level of schools.
ggplot(data = E, aes(x = StageID, fill = ParentAnsweringSurvey)) + geom_bar() +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(StageID, ParentAnsweringSurvey) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(StageID) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = StageID, y = count)) +
geom_bar(aes(fill = ParentAnsweringSurvey), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized StageID Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.2))
Both highschool and middleschool have more than 60% parent answering survey. Lowerlevel has less than 50% rate of parent answering survey.
ggplot(data = E, aes(x = StageID, fill = ParentschoolSatisfaction)) + geom_bar() +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(StageID, ParentschoolSatisfaction) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(StageID) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = StageID, y = count)) +
geom_bar(aes(fill = ParentschoolSatisfaction), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized StageID Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.2))
Highschool has the lowest rate(under 60%) of good parent school satisfaction. Both middleschool and lowerlevel have more than 60% rate of good parent school satisfaction.
ggplot(data = E, aes(x = StageID, fill = StudentAbsenceDays)) + geom_bar() +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Stage Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(StageID, StudentAbsenceDays) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(StageID) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = StageID, y = count)) +
geom_bar(aes(fill = StudentAbsenceDays), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized StageID Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.2))
Lowerlevel has higher rate of above 7 days of student absence.
ggplot(data = E, aes(x = StageID, fill = Class)) + geom_bar() +
labs(x = "StageID", y = "Student Count") + ggtitle(label = "Grade Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ESummary = E %>%
# Get the counts
group_by(StageID, Class) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(StageID) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = StageID, y = count)) +
geom_bar(aes(fill = Class), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized StageID Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,1,0.2))
Lower level has the highest rate of low class students.Highschool has the highest rate of high class students. Middle school has the highest rate of medium class students.
Grade ID distribution:
ggplot(data = E, aes(x = GradeID, fill = StageID)) + geom_bar() +
labs(x = "GradeID", y = "Student Count") +theme(axis.text.x = element_text(angle=90, hjust=1))+ ggtitle(label = "Grade Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Grade 2, 3, 4 and 5 belong to Lowerlevel, grade 6,7 and 8 belong to Middle school, grade 9,10,11 and 12 belong to High school.
Section ID distribution:
ggplot(data = E, aes(x = SectionID, fill = StageID)) + geom_bar() +
labs(x = "SectionID", y = "Student Count") + ggtitle(label = "Section Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Section C has only Middle school and Lowerlevel students
ggplot(data = E, aes(x = SectionID, fill = Topic)) + geom_bar(colour = "black") +
labs(x = "SectionID", y = "Student Count")+ ggtitle(label = "Section Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Section C has only IT and science students.
Topic distribution:
ggplot(data = E, aes(x = Topic, fill = gender)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1)) 
Spanish has fewest girls, whereas Science, Chemistry, English and French have higher ratio of girls.
ggplot(data = E, aes(x = Topic, fill = Class)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
Geology has no low class students
ESummary = E %>%
# Get the counts
group_by(Topic, Class) %>%
summarise(count = n()) %>%
# Get labels and position of labels
group_by(Topic) %>%
mutate(Percent = paste0(sprintf("%.1f", count/sum(count) * 100),"%"))
ggplot(ESummary, aes(x = Topic, y = count)) +
geom_bar(aes(fill = Class), stat ="identity", position = 'fill')+ labs(x = "Topic", y = "Student Count")+ ggtitle(label = "Normalized Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
Biology has the highest ratio of high class students. It has the highest ratio of low class students. Geology has the highest ratio of medium class students.
ggplot(data = E, aes(x = Topic, fill = NationalITy)) + geom_bar(colour = "black") +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))
IT has most people from Kuwait. Chemistry has least diversity. French has most diversity.
ggplot(data = E, aes(x = Topic, fill = StageID)) + geom_bar(colour = "black") +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))+ scale_y_continuous(breaks = seq(0,100,4))
Geology, biology and chemistry are only in middle school. French has the lowest rate of highschool student.
ggplot(data = E, aes(x = Topic, fill = SectionID)) + geom_bar(colour = "black") +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))+ scale_y_continuous(breaks = seq(0,100,4))
Section C only has Spanish and IT students.
ggplot(data = E, aes(x = Topic, fill = Semester)) + geom_bar(colour = "black") +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))+ scale_y_continuous(breaks = seq(0,100,4))
IT mostly has students who are in first semester.
ggplot(data = E, aes(x = Topic, fill = Relation)) + geom_bar(colour = "black") +
labs(x = "Topic", y = "Student Count") + ggtitle(label = "Topic Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+theme(axis.text.x = element_text(angle=90, hjust=1))+ scale_y_continuous(breaks = seq(0,100,4))
Most French students have mom as guardian whereas most IT students have fathers as guardian.
Parent Answering Survey distribution:
ggplot(data = E, aes(x = ParentAnsweringSurvey, fill = ParentschoolSatisfaction)) + geom_bar(colour = "black") +
labs(x = "ParentAnsweringSurvey", y = "Student Count") + ggtitle(label = "ParentAnsweringSurvey Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,500,20))
Most of the parents who aren’t satisfied with the school do not answer the survey.
Class distribution:
ggplot(data = E, aes(x = Class, fill = gender)) + geom_bar(colour = "black") +
labs(x = "Class", y = "Student Count") + ggtitle(label = "Class Distribution")+theme_grey()+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,500,20))
Very few girls in low class
ggplot(data = E, aes(x = Class, fill = Relation)) + geom_bar(colour = "black") +
labs(x = "Class", y = "Student Count") + ggtitle(label = "Class Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,500,20))
The students who have moms as guardians have higher chances to get high class marks.
ggplot(data = E, aes(x = Class, fill = ParentAnsweringSurvey)) + geom_bar(colour = "black") +
labs(x = "Class", y = "Student Count") + ggtitle(label = "Class Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,500,20))
Students whose parents answer the survey are the ones getting good marks.
ggplot(data = E, aes(x = Class, fill = StudentAbsenceDays)) + geom_bar(colour = "black") +
labs(x = "Class", y = "Student Count") + ggtitle(label = "Class Distribution")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+ scale_y_continuous(breaks = seq(0,500,20))
Student getting absent are the ones getting low marks.
Summary of Task 1.2: Based on the exploration work above, We conclude the following main findings which will be much useful for analysis and prediction of the academic performance in the next steps:
1. Girls have higher rate in high class but lower rate in low class, boys are the opposite. Girls’ parents have higher school satisfaction than boys. Boys has higher rate of above 7 absence days. Very few girls in low class.
2. The differences of class performance between place of birth and nationality are very tiny.The findings between pace of birth and nationality are very similar. It’s enough for us to just use nationality instead of both for the next steps.
3. Middle school has the most students. Lower-level has higher rate of above 7 days of student absence. Lower-level has the highest rate of low class students. Highschool has the highest rate of high class students. Middle school has the highest rate of medium class students.
4. Biology has the highest ratio of high class students. It has the highest ratio of low class students. Geology has the highest ratio of medium class students.
5. The students who have moms as guardians have higher chances to get high class marks. Students whose parents answer the survey are the ones getting good marks. Student getting absent are the ones getting low marks.
Task 1.3. Histogram of numerical values distribution:
ggplot(E, aes(x = raisedhands, fill = Class)) +
geom_histogram(binwidth = 5, colour = "black") +
scale_x_continuous(name = "Raised Hands",
breaks = seq(0, 100, 5),
limits=c(0, 100)) +
scale_y_continuous(name = "Count") +
ggtitle("Frequency of Raised Hands") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students have the higher class the more raised hands.
ggplot(data = E, aes(x = raisedhands, color = Class)) + geom_density(size=1) + ggtitle("Frequency of Raised Hands") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Most of the high class students raised hands many times, most of the low class students raised hands very few times.
ggplot(E, aes(x = VisITedResources, fill = Class)) +
geom_histogram( binwidth = 5, colour = "black") +
scale_x_continuous(name = "VisITedResources",
breaks = seq(0, 100, 5),
limits=c(0, 100)) +
scale_y_continuous(name = "Count") +
ggtitle("Frequency histogram of VisITedResources")+theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students who have the higher class visited the resources more times.
ggplot(data = E, aes(x = VisITedResources, color = Class)) + geom_density(size=1) + ggtitle("Frequency of VisITedResources") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Most of the students with high class visited the resources many times, most of the students with low class visited the resources very few times.
ggplot(E, aes(x = AnnouncementsView, fill = Class)) +
geom_histogram( binwidth = 5, colour = "black") +
scale_x_continuous(name = "AnnouncementsView",
breaks = seq(0, 100, 5),
limits=c(0, 100)) +
scale_y_continuous(name = "Count") +
ggtitle("Frequency histogram of AnnouncementsView") 
Students who have the higher class viewed the announcements more times.
ggplot(data = E, aes(x = AnnouncementsView, color = Class)) + geom_density(size=1) + ggtitle("Frequency of AnnouncementsView") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Most of the students with high class viewed the announcements many times, most of the students with low class viewed the announcements very few times. But the differences between students with high class and medium class is not very obvious
ggplot(E, aes(x = Discussion, fill = Class)) +
geom_histogram(binwidth = 5, colour = "black") +
scale_x_continuous(name = "Discussion",
breaks = seq(0, 100, 5),
limits=c(0, 100)) +
scale_y_continuous(name = "Count") +
ggtitle("Frequency histogram of Discussion") 
Students who have the higher class discussed more times.
ggplot(data = E, aes(x = Discussion, color = Class)) + geom_density(size=1) + ggtitle("Frequency of Discussion") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Most of the students with high class discussed many times, most of the students with low class discussed very few times.
Summary of Task 1.3: From the exploration of the numerical feature values, we have the following findings:
1 .Students who have the higher class raised hands, visited resource, viewd announcements and discussed more times.
2. Most of the students with high class raised hands, visited resource, viewed announcements and discussed many times, most of the students with low class raised hands, visited resource, viewed announcements and discussed few times.
3. There are some little differences in the density curve, especially in the announcement view and discussion.
Task 2. Exploration of the relationships between different features of the students’ academic performance datasets
Class and the numerial features
ggplot(data = E, aes(x = Class, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Class") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
The higher class the higher times of raising hands
ggplot(data = E, aes(x = Class, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Class") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
The higher class the higher times of visiting resources
ggplot(data = E, aes(x = Class, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Class") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
The higher class the higher times of viewing announcements
ggplot(data = E, aes(x = Class, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Class") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
The higher class the higher times of discussion
Gender and the numerial features
ggplot(data = E, aes(x = gender, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Gender") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Girls have more hand raises
ggplot(data = E, aes(x = gender, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Gender") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Girls visit more resources
ggplot(data = E, aes(x = gender, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Gender") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Girls view more announcements, but the differences are not very obvious.
ggplot(data = E, aes(x = gender, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Gender") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Girls have more discussion, but the differences are not very obvious.
Nationality and the numerial features
ggplot(data = E, aes(x = NationalITy, y = raisedhands)) + geom_boxplot()+
theme(axis.text.x = element_text(angle=45, hjust=1))+ ggtitle("Relationship of Nationality") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Jordan has more hand raises than Kuwait, Lybia has lowest. Iraq and Palestine have highest hand raises.
ggplot(data = E, aes(x = NationalITy, y = VisITedResources)) + geom_boxplot()+
theme(axis.text.x = element_text(angle=45, hjust=1))+ ggtitle("Relationship of Nationality") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Jordan has more visiting resouces than Kuwait, Lybia has lowest. Iraq and Palestine have highest visiting resources.
ggplot(data = E, aes(x = NationalITy, y = AnnouncementsView)) + geom_boxplot()+
theme(axis.text.x = element_text(angle=45, hjust=1))+ ggtitle("Relationship of Nationality") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Jordan has more announcement view than Kuwait, Lybia has lowest. Iraq and Palestine have highest announcement view.
ggplot(data = E, aes(x = NationalITy, y = Discussion)) + geom_boxplot()+
theme(axis.text.x = element_text(angle=45, hjust=1))+ ggtitle("Relationship of Nationality") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
The differences between Jordan and Kuwait are not obvious, Lybia has lowest. Iraq and Palestine have highest announcement view.
Stage ID and the numerial features
ggplot(data = E, aes(x = StageID, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Stage ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
More hand raises in middle schools.
ggplot(data = E, aes(x = StageID, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Stage ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
#More hand raises in middle schools.More visiting resouces in middle schools.
ggplot(data = E, aes(x = StageID, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Stage ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
More announcement views in middle schools.
ggplot(data = E, aes(x = StageID, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Stage ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
More discussions in high schools.
Topic and the numerial features
ggplot(data = E, aes(x = Topic, y = raisedhands)) + geom_boxplot()+ geom_boxplot()+ ggtitle("Relationship of Topic") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
IT has very few hand raises intersetingly although most students study there.
ggplot(data = E, aes(x = Topic, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Topic") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ggplot(data = E, aes(x = Topic, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Topic") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
IT has very few announcement view although most students study there.
ggplot(data = E, aes(x = Topic, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Topic") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
IT has very few discussion although most students study there.
Section ID and the numerial features
ggplot(data = E, aes(x = SectionID, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Section ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ggplot(data = E, aes(x = SectionID, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Section ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ggplot(data = E, aes(x = SectionID, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Section ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
ggplot(data = E, aes(x = SectionID, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Section ID") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Section A has the highest raised hands, visited resources, announcements view and discussion, whereas secion C has the lowest raised hands, visited resources, announcements view and discussion.
Semester and the numerial features
ggplot(data = E, aes(x = Semester, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Semester") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Second semester has more hand raises
ggplot(data = E, aes(x = Semester, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Semester") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Second semester has more visited resources
ggplot(data = E, aes(x = Semester, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Semester") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Second semester has more announcements view
ggplot(data = E, aes(x = Semester, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Semester") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Almost no differences between these two semesters in discussion.
Relation and the numerial features
ggplot(data = E, aes(x = Relation, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Relation") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students with Guardians mother have more hand raises.
ggplot(data = E, aes(x = Relation, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Relation") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students with Guardians mother have more visited resources.
ggplot(data = E, aes(x = Relation, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Relation") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students with Guardians mother have more announcements view.
ggplot(data = E, aes(x = Relation, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Relation") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
No obvious differences between students with differen guardians in discussion.
Parent Answering Survey and the numerial features
ggplot(data = E, aes(x = ParentAnsweringSurvey, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Parent Answering Survey") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parent answering survey have more hand raises
ggplot(data = E, aes(x = ParentAnsweringSurvey, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Parent Answering Survey") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parent answering survey have more visited resources.
ggplot(data = E, aes(x = ParentAnsweringSurvey, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Parent Answering Survey") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parent answering survey have more announcements view.
ggplot(data = E, aes(x = ParentAnsweringSurvey, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Parent Answering Survey") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parent answering survey have more discussion
Parent school Satisfaction and the numerial features
ggplot(data = E, aes(x = ParentschoolSatisfaction, y = raisedhands)) + geom_boxplot()+ ggtitle("Relationship of Parent Satisfacion") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parents with good satisfaction have more hand raises.
ggplot(data = E, aes(x = ParentschoolSatisfaction, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Parent Satisfacion") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parents with good satisfaction have more visited resources.
ggplot(data = E, aes(x = ParentschoolSatisfaction, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Parent Satisfacion") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students whose parents with good satisfaction have more announcement view.
ggplot(data = E, aes(x = ParentschoolSatisfaction, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Parent Satisfacion") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
No obvious differences in discution between students whose parents with different satisfaction .
Student Absence Days and the numerial features
ggplot(data = E, aes(x = StudentAbsenceDays, y = raisedhands)) + geom_boxplot() + ggtitle("Relationship of Student Absence Days") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students have the more leaves the less hand raises.
ggplot(data = E, aes(x = StudentAbsenceDays, y = VisITedResources)) + geom_boxplot()+ ggtitle("Relationship of Student Absence Days") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students have the more leaves the less visited resources.
ggplot(data = E, aes(x = StudentAbsenceDays, y = AnnouncementsView)) + geom_boxplot()+ ggtitle("Relationship of Student Absence Days") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students have the more leaves the less announcements view.
ggplot(data = E, aes(x = StudentAbsenceDays, y = Discussion)) + geom_boxplot()+ ggtitle("Relationship of Student Absence Days") +theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))
Students have the more leaves the less discussion.
Summary of Task 2: From the exploration relationship between features, we have the following findings:
1. From the relationship between class and other features, it further confirmed our findings in the previous step: Students who have the higher class raised hands, visited resource, viewed announcements and discussed more times.
2. Girls have more hand raises and visit more resources.
3. The results on raised hands, visited resources and announcements view are very similar. The difference of discussions between Jordan and Kuwait is not obvious.
4. More raised hands, visited resources and announcements view in middle schools. More discussions in high schools.
5. IT has very few hand raises interestingly although most students study there.
6. Students with Guardians mother have more hand raises, visited resources and announcements view.
7. Students whose parent answering survey have more hand raises, visited resources, announcements view and discussion.
8. Students whose parents with good satisfaction have more raised hands, visited resources and announcements.
9. Students have the more leaves the less raised hands, visited resources, announcements and discussion.
Task 3. Prediction of the students’ academic performance. Build prediction model and evaluate the results of the model
Preprocess: Select the important features which are important to Class, and remove some unimportant features.
#create data with some correlation structure
library(corrplot)## Warning: package 'corrplot' was built under R version 3.4.2Edu1 <- E
for(i in 1:ncol(Edu1)){
Edu1[,i]<-as.integer(Edu1[,i])
}
corrplot::corrplot(cor(as.matrix(Edu1)),type = "upper", method="circle", tl.pos="lt",
tl.col="black", tl.cex=0.6, tl.srt=45,
addCoef.col="black", addCoefasPercent = TRUE,
sig.level=0.50, insig = "blank") 
From the correlation plot above, we can find that features like raisedhands, visitedresources,announcementsview,studentabsencedays and discussion have very close correlation with class. On the other hand, some features like sectionID, gradeID, stageID,placeofbirth and nationality have very less correlation with class. So we decide to remove useless features before building our prediction models.
# Remove some useless features , such as: GradeID, SectionID and Semester.
E1 <- E[,-c(5,6,8)]
E1## gender NationalITy PlaceofBirth StageID Topic Relation
## 1 M KW KuwaIT Lowerlevel IT Father
## 2 M KW KuwaIT Lowerlevel IT Father
## 3 M KW KuwaIT Lowerlevel IT Father
## 4 M KW KuwaIT Lowerlevel IT Father
## 5 M KW KuwaIT Lowerlevel IT Father
## 6 F KW KuwaIT Lowerlevel IT Father
## 7 M KW KuwaIT MiddleSchool Math Father
## 8 M KW KuwaIT MiddleSchool Math Father
## 9 F KW KuwaIT MiddleSchool Math Father
## 10 F KW KuwaIT MiddleSchool IT Father
## 11 M KW KuwaIT MiddleSchool Math Father
## 12 M KW KuwaIT MiddleSchool Math Father
## 13 M KW KuwaIT Lowerlevel IT Father
## 14 M lebanon lebanon MiddleSchool Math Father
## 15 F KW KuwaIT MiddleSchool Math Mum
## 16 F KW KuwaIT MiddleSchool IT Father
## 17 M KW KuwaIT MiddleSchool IT Father
## 18 M KW KuwaIT MiddleSchool Math Father
## 19 F KW KuwaIT MiddleSchool IT Mum
## 20 M KW KuwaIT MiddleSchool IT Mum
## 21 F KW KuwaIT MiddleSchool IT Father
## 22 F KW KuwaIT MiddleSchool IT Father
## 23 M KW KuwaIT MiddleSchool IT Father
## 24 M KW KuwaIT MiddleSchool IT Father
## 25 M KW KuwaIT MiddleSchool IT Father
## 26 M KW KuwaIT MiddleSchool IT Father
## 27 M KW KuwaIT MiddleSchool IT Father
## 28 M KW KuwaIT MiddleSchool Arabic Father
## 29 M KW KuwaIT MiddleSchool Science Father
## 30 F KW KuwaIT MiddleSchool Arabic Father
## 31 F KW KuwaIT MiddleSchool Arabic Father
## 32 M KW KuwaIT MiddleSchool IT Father
## 33 F KW KuwaIT Lowerlevel IT Father
## 34 M KW KuwaIT Lowerlevel English Father
## 35 M KW KuwaIT MiddleSchool Science Father
## 36 M KW KuwaIT MiddleSchool English Father
## 37 M KW KuwaIT MiddleSchool Science Mum
## 38 F Egypt Egypt MiddleSchool IT Father
## 39 M KW KuwaIT MiddleSchool IT Father
## 40 F SaudiArabia SaudiArabia MiddleSchool Science Father
## 41 F KW KuwaIT MiddleSchool IT Father
## 42 M KW KuwaIT MiddleSchool IT Father
## 43 M KW KuwaIT HighSchool IT Father
## 44 F KW KuwaIT HighSchool IT Father
## 45 F KW KuwaIT HighSchool IT Father
## 46 M KW KuwaIT MiddleSchool Quran Father
## 47 M KW KuwaIT Lowerlevel English Father
## 48 F KW KuwaIT HighSchool English Mum
## 49 F KW KuwaIT HighSchool English Mum
## 50 F KW KuwaIT HighSchool English Father
## 51 F KW KuwaIT HighSchool English Father
## 52 M KW KuwaIT HighSchool English Father
## 53 F KW KuwaIT HighSchool English Father
## 54 F KW KuwaIT HighSchool Science Mum
## 55 M KW KuwaIT HighSchool English Mum
## 56 M KW KuwaIT MiddleSchool Math Father
## 57 M USA USA MiddleSchool Math Father
## 58 M KW KuwaIT MiddleSchool Math Father
## 59 M KW KuwaIT MiddleSchool Math Father
## 60 F USA USA MiddleSchool Math Father
## 61 F Jordan Jordan Lowerlevel English Mum
## 62 M KW KuwaIT HighSchool IT Father
## 63 M venzuela venzuela HighSchool IT Mum
## 64 M KW KuwaIT HighSchool IT Father
## 65 M KW KuwaIT HighSchool IT Father
## 66 M KW KuwaIT HighSchool English Father
## 67 F KW KuwaIT HighSchool English Father
## 68 F USA USA HighSchool English Mum
## 69 F USA USA HighSchool IT Mum
## 70 M Jordan Jordan MiddleSchool IT Father
## 71 M Jordan Jordan MiddleSchool IT Father
## 72 M KW KuwaIT MiddleSchool IT Father
## 73 M KW KuwaIT MiddleSchool Math Father
## 74 F KW KuwaIT MiddleSchool English Father
## 75 M KW KuwaIT MiddleSchool IT Father
## 76 M Jordan Jordan MiddleSchool Quran Mum
## 77 M Iran Iran HighSchool IT Mum
## 78 M KW KuwaIT HighSchool IT Father
## 79 M KW KuwaIT HighSchool Quran Father
## 80 F lebanon lebanon MiddleSchool Math Mum
## 81 M KW KuwaIT MiddleSchool Math Father
## 82 M KW KuwaIT Lowerlevel IT Father
## 83 M KW KuwaIT Lowerlevel IT Father
## 84 M KW KuwaIT Lowerlevel IT Father
## 85 M KW KuwaIT Lowerlevel IT Father
## 86 M KW KuwaIT Lowerlevel IT Father
## 87 M SaudiArabia SaudiArabia Lowerlevel IT Father
## 88 M KW KuwaIT Lowerlevel IT Father
## 89 F KW KuwaIT Lowerlevel IT Father
## 90 M KW KuwaIT Lowerlevel IT Father
## 91 M KW KuwaIT Lowerlevel IT Father
## 92 M KW KuwaIT Lowerlevel IT Father
## 93 F KW KuwaIT Lowerlevel IT Father
## 94 M USA USA Lowerlevel IT Father
## 95 F KW KuwaIT Lowerlevel IT Father
## 96 F KW KuwaIT Lowerlevel IT Father
## 97 F KW KuwaIT Lowerlevel IT Father
## 98 F KW KuwaIT Lowerlevel IT Father
## 99 F KW KuwaIT Lowerlevel IT Father
## 100 M KW KuwaIT Lowerlevel IT Father
## 101 M KW KuwaIT Lowerlevel IT Father
## 102 F KW KuwaIT Lowerlevel IT Mum
## 103 F KW KuwaIT Lowerlevel IT Father
## 104 M KW KuwaIT Lowerlevel IT Father
## 105 M KW KuwaIT Lowerlevel IT Father
## 106 F KW KuwaIT Lowerlevel IT Father
## 107 F KW KuwaIT Lowerlevel IT Father
## 108 M KW KuwaIT Lowerlevel IT Father
## 109 M SaudiArabia SaudiArabia Lowerlevel IT Father
## 110 M KW KuwaIT Lowerlevel IT Father
## 111 F Jordan Jordan Lowerlevel IT Mum
## 112 M KW KuwaIT Lowerlevel IT Father
## 113 M KW KuwaIT Lowerlevel IT Father
## 114 M KW KuwaIT Lowerlevel IT Father
## 115 M KW KuwaIT Lowerlevel IT Father
## 116 M KW KuwaIT Lowerlevel IT Father
## 117 F KW KuwaIT Lowerlevel IT Mum
## 118 F KW KuwaIT Lowerlevel IT Mum
## 119 F KW KuwaIT Lowerlevel IT Mum
## 120 M KW KuwaIT Lowerlevel IT Father
## 121 M KW KuwaIT Lowerlevel IT Mum
## 122 M KW KuwaIT Lowerlevel IT Mum
## 123 F KW KuwaIT Lowerlevel IT Father
## 124 F lebanon lebanon Lowerlevel IT Father
## 125 M KW KuwaIT Lowerlevel IT Father
## 126 M KW KuwaIT Lowerlevel IT Father
## 127 F Iran Iran Lowerlevel IT Father
## 128 M KW KuwaIT Lowerlevel IT Father
## 129 M KW KuwaIT Lowerlevel IT Father
## 130 M KW KuwaIT Lowerlevel IT Father
## 131 F KW KuwaIT Lowerlevel IT Father
## 132 M Jordan Jordan Lowerlevel IT Father
## 133 M KW KuwaIT Lowerlevel IT Father
## 134 M KW KuwaIT Lowerlevel IT Father
## 135 M KW KuwaIT MiddleSchool Quran Father
## 136 F Egypt Egypt MiddleSchool Quran Mum
## 137 F KW USA HighSchool Quran Mum
## 138 F KW USA HighSchool Quran Mum
## 139 F Jordan Egypt MiddleSchool Quran Mum
## 140 M Jordan USA MiddleSchool Quran Mum
## 141 M Tunis Tunis MiddleSchool Quran Father
## 142 M Tunis Tunis MiddleSchool Quran Father
## 143 M Tunis Tunis MiddleSchool Quran Father
## 144 M KW KuwaIT MiddleSchool Quran Father
## 145 M KW KuwaIT MiddleSchool Quran Father
## 146 M Tunis SaudiArabia MiddleSchool Quran Father
## 147 F lebanon lebanon MiddleSchool Quran Mum
## 148 F lebanon lebanon MiddleSchool Quran Father
## 149 F Jordan Jordan MiddleSchool Quran Father
## 150 F Jordan lebanon MiddleSchool Quran Mum
## 151 M SaudiArabia SaudiArabia HighSchool Quran Father
## 152 M SaudiArabia USA HighSchool Science Father
## 153 F SaudiArabia USA HighSchool Science Father
## 154 M SaudiArabia USA HighSchool Spanish Father
## 155 M SaudiArabia USA HighSchool English Father
## 156 M SaudiArabia USA HighSchool Math Father
## 157 F Tunis USA HighSchool French Father
## 158 M Tunis Tunis HighSchool Science Father
## 159 M Tunis KuwaIT MiddleSchool Science Father
## 160 F KW KuwaIT MiddleSchool Science Father
## 161 F Jordan Jordan MiddleSchool Science Mum
## 162 M Jordan Jordan MiddleSchool Science Mum
## 163 F Jordan Jordan Lowerlevel French Mum
## 164 F Jordan USA Lowerlevel French Mum
## 165 M Jordan Jordan Lowerlevel French Mum
## 166 F Morocco Morocco Lowerlevel French Mum
## 167 M KW KuwaIT Lowerlevel French Mum
## 168 M Tunis Tunis Lowerlevel French Mum
## 169 M KW KuwaIT Lowerlevel French Mum
## 170 M KW KuwaIT Lowerlevel French Mum
## 171 M KW KuwaIT Lowerlevel French Father
## 172 M KW KuwaIT Lowerlevel French Mum
## 173 M Iran Iran Lowerlevel French Mum
## 174 M Syria Syria Lowerlevel French Mum
## 175 F Jordan Jordan Lowerlevel French Father
## 176 M Iran Iran Lowerlevel French Father
## 177 M Tunis Tunis Lowerlevel French Mum
## 178 F USA USA Lowerlevel French Mum
## 179 F lebanon lebanon Lowerlevel French Mum
## 180 M lebanon lebanon Lowerlevel French Mum
## 181 F SaudiArabia SaudiArabia Lowerlevel French Father
## 182 M Jordan Jordan Lowerlevel French Father
## 183 M KW KuwaIT MiddleSchool Arabic Mum
## 184 F lebanon lebanon MiddleSchool Arabic Father
## 185 M lebanon lebanon MiddleSchool Arabic Mum
## 186 M lebanon Jordan MiddleSchool Arabic Mum
## 187 M KW KuwaIT MiddleSchool Arabic Mum
## 188 F KW KuwaIT MiddleSchool Arabic Father
## 189 M KW KuwaIT MiddleSchool Arabic Mum
## 190 F KW KuwaIT MiddleSchool Arabic Father
## 191 M KW KuwaIT MiddleSchool Arabic Father
## 192 M KW KuwaIT MiddleSchool Arabic Father
## 193 M KW KuwaIT MiddleSchool Arabic Mum
## 194 M Morocco Morocco MiddleSchool Arabic Mum
## 195 M Morocco Morocco MiddleSchool Arabic Mum
## 196 M KW KuwaIT MiddleSchool Arabic Father
## 197 M KW KuwaIT MiddleSchool Arabic Mum
## 198 M KW KuwaIT MiddleSchool Arabic Mum
## 199 M Morocco Morocco MiddleSchool Arabic Father
## 200 M KW KuwaIT MiddleSchool Arabic Father
## 201 M KW KuwaIT MiddleSchool Arabic Mum
## 202 F lebanon lebanon MiddleSchool Arabic Mum
## 203 M KW KuwaIT MiddleSchool Arabic Father
## 204 F KW KuwaIT MiddleSchool Arabic Father
## 205 M Jordan Jordan MiddleSchool Arabic Father
## 206 F SaudiArabia SaudiArabia MiddleSchool Arabic Mum
## 207 M KW KuwaIT MiddleSchool Arabic Father
## 208 M KW KuwaIT MiddleSchool Spanish Father
## 209 M Tunis Tunis MiddleSchool Spanish Mum
## 210 M KW KuwaIT MiddleSchool Spanish Father
## 211 M Jordan SaudiArabia MiddleSchool Spanish Mum
## 212 M KW KuwaIT MiddleSchool Spanish Father
## 213 M Tunis Tunis MiddleSchool Spanish Mum
## 214 M Tunis Tunis MiddleSchool Spanish Father
## 215 M KW KuwaIT MiddleSchool Spanish Father
## 216 M KW KuwaIT MiddleSchool Spanish Father
## 217 M Iran Iran MiddleSchool Spanish Mum
## 218 F Jordan KuwaIT MiddleSchool Spanish Father
## 219 M KW KuwaIT MiddleSchool Spanish Mum
## 220 M KW KuwaIT MiddleSchool Spanish Mum
## 221 M lebanon lebanon MiddleSchool Spanish Father
## 222 M lebanon lebanon MiddleSchool Spanish Father
## 223 M KW KuwaIT MiddleSchool Spanish Father
## 224 M KW KuwaIT MiddleSchool Spanish Mum
## 225 M KW KuwaIT MiddleSchool Spanish Mum
## 226 M KW KuwaIT MiddleSchool Spanish Mum
## 227 M KW KuwaIT MiddleSchool Spanish Father
## 228 M KW KuwaIT MiddleSchool Spanish Father
## 229 M KW KuwaIT HighSchool Math Mum
## 230 M KW KuwaIT MiddleSchool Spanish Father
## 231 M Iran Iran MiddleSchool Spanish Mum
## 232 M Syria Jordan MiddleSchool Spanish Father
## 233 M Syria Syria MiddleSchool Quran Father
## 234 F Jordan Jordan MiddleSchool Science Mum
## 235 M KW KuwaIT MiddleSchool Science Mum
## 236 M KW KuwaIT MiddleSchool Science Father
## 237 M Jordan Jordan MiddleSchool Science Father
## 238 M KW KuwaIT MiddleSchool Science Father
## 239 F KW KuwaIT MiddleSchool Science Mum
## 240 M Egypt Egypt MiddleSchool Science Mum
## 241 M KW KuwaIT MiddleSchool Science Mum
## 242 M Egypt Egypt MiddleSchool Science Father
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## ParentAnsweringSurvey ParentschoolSatisfaction StudentAbsenceDays
## 1 Yes Good Under-7
## 2 Yes Good Under-7
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## 20 Yes Good Under-7
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## 23 No Bad Under-7
## 24 No Bad Above-7
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## 26 Yes Good Above-7
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## 32 Yes Good Above-7
## 33 Yes Good Above-7
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## 170 No Bad Under-7
## 171 Yes Good Under-7
## 172 No Good Under-7
## 173 No Good Above-7
## 174 Yes Good Above-7
## 175 Yes Bad Above-7
## 176 No Bad Above-7
## 177 Yes Bad Under-7
## 178 Yes Bad Under-7
## 179 Yes Bad Under-7
## 180 No Bad Under-7
## 181 Yes Bad Under-7
## 182 No Bad Above-7
## 183 Yes Bad Under-7
## 184 No Bad Under-7
## 185 No Bad Above-7
## 186 Yes Good Under-7
## 187 Yes Good Under-7
## 188 Yes Good Under-7
## 189 Yes Good Under-7
## 190 No Bad Under-7
## 191 No Bad Under-7
## 192 Yes Good Above-7
## 193 Yes Good Under-7
## 194 No Bad Under-7
## 195 No Bad Under-7
## 196 Yes Good Under-7
## 197 Yes Good Under-7
## 198 No Bad Under-7
## 199 No Bad Above-7
## 200 No Bad Under-7
## 201 Yes Bad Under-7
## 202 Yes Good Under-7
## 203 Yes Good Under-7
## 204 Yes Bad Under-7
## 205 No Bad Above-7
## 206 Yes Bad Above-7
## 207 Yes Good Under-7
## 208 Yes Good Under-7
## 209 No Bad Under-7
## 210 No Bad Under-7
## 211 Yes Good Under-7
## 212 No Bad Under-7
## 213 Yes Good Under-7
## 214 No Bad Above-7
## 215 No Bad Above-7
## 216 No Good Above-7
## 217 Yes Bad Above-7
## 218 Yes Good Under-7
## 219 Yes Bad Under-7
## 220 Yes Good Under-7
## 221 Yes Good Under-7
## 222 No Good Under-7
## 223 Yes Bad Under-7
## 224 Yes Good Under-7
## 225 Yes Bad Under-7
## 226 No Bad Under-7
## 227 No Bad Under-7
## 228 Yes Good Under-7
## 229 Yes Good Above-7
## 230 No Bad Above-7
## 231 No Bad Above-7
## 232 Yes Good Above-7
## 233 Yes Good Above-7
## 234 Yes Good Above-7
## 235 Yes Good Above-7
## 236 No Bad Above-7
## 237 No Bad Above-7
## 238 Yes Good Above-7
## 239 Yes Good Above-7
## 240 Yes Good Under-7
## 241 Yes Bad Under-7
## 242 Yes Good Under-7
## 243 No Bad Above-7
## 244 No Bad Above-7
## 245 Yes Bad Under-7
## 246 Yes Bad Under-7
## 247 Yes Good Under-7
## 248 No Bad Above-7
## 249 No Bad Above-7
## 250 No Good Above-7
## 251 Yes Good Under-7
## 252 No Good Above-7
## 253 No Bad Under-7
## 254 No Bad Above-7
## 255 Yes Good Under-7
## 256 Yes Good Under-7
## 257 No Good Under-7
## 258 No Good Under-7
## 259 Yes Good Under-7
## 260 No Bad Above-7
## 261 No Bad Above-7
## 262 Yes Bad Above-7
## 263 Yes Good Above-7
## 264 No Bad Above-7
## 265 No Bad Under-7
## 266 No Bad Under-7
## 267 Yes Good Above-7
## 268 Yes Good Above-7
## 269 Yes Bad Above-7
## 270 Yes Bad Above-7
## 271 No Good Under-7
## 272 No Good Under-7
## 273 No Bad Under-7
## 274 No Bad Under-7
## 275 Yes Good Under-7
## 276 Yes Good Under-7
## 277 Yes Good Under-7
## 278 Yes Good Under-7
## 279 No Good Under-7
## 280 No Good Under-7
## 281 No Good Under-7
## 282 No Good Under-7
## 283 No Good Under-7
## 284 No Good Under-7
## 285 No Bad Above-7
## 286 Yes Bad Above-7
## 287 Yes Good Under-7
## 288 Yes Good Under-7
## 289 Yes Good Under-7
## 290 Yes Good Under-7
## 291 No Bad Above-7
## 292 No Bad Above-7
## 293 Yes Good Under-7
## 294 Yes Good Under-7
## 295 Yes Good Above-7
## 296 Yes Good Above-7
## 297 No Good Under-7
## 298 No Good Under-7
## 299 No Good Above-7
## 300 No Good Above-7
## 301 No Bad Above-7
## 302 No Bad Above-7
## 303 No Bad Under-7
## 304 No Bad Under-7
## 305 Yes Bad Under-7
## 306 Yes Bad Under-7
## 307 Yes Good Under-7
## 308 Yes Good Under-7
## 309 Yes Good Above-7
## 310 Yes Good Above-7
## 311 No Good Above-7
## 312 No Good Above-7
## 313 Yes Good Under-7
## 314 Yes Good Under-7
## 315 Yes Good Under-7
## 316 Yes Good Under-7
## 317 Yes Good Under-7
## 318 Yes Good Under-7
## 319 Yes Good Under-7
## 320 Yes Good Under-7
## 321 Yes Good Above-7
## 322 Yes Good Above-7
## 323 No Bad Above-7
## 324 No Bad Above-7
## 325 No Bad Under-7
## 326 No Bad Under-7
## 327 No Bad Above-7
## 328 No Bad Above-7
## 329 Yes Good Under-7
## 330 Yes Good Under-7
## 331 No Good Above-7
## 332 No Good Above-7
## 333 No Good Under-7
## 334 No Good Under-7
## 335 No Good Above-7
## 336 No Good Above-7
## 337 No Good Under-7
## 338 No Good Under-7
## 339 No Good Under-7
## 340 No Good Under-7
## 341 No Good Under-7
## 342 No Good Under-7
## 343 No Bad Under-7
## 344 No Bad Under-7
## 345 No Good Under-7
## 346 No Good Under-7
## 347 No Good Under-7
## 348 No Good Under-7
## 349 No Good Above-7
## 350 No Good Above-7
## 351 No Good Above-7
## 352 No Good Above-7
## 353 Yes Good Under-7
## 354 Yes Good Under-7
## 355 Yes Good Under-7
## 356 Yes Good Under-7
## 357 Yes Good Under-7
## 358 Yes Good Under-7
## 359 Yes Good Under-7
## 360 Yes Good Under-7
## 361 Yes Good Above-7
## 362 Yes Good Above-7
## 363 Yes Good Under-7
## 364 Yes Good Under-7
## 365 Yes Good Under-7
## 366 Yes Good Under-7
## 367 Yes Good Above-7
## 368 Yes Good Above-7
## 369 No Good Under-7
## 370 No Good Under-7
## 371 No Bad Under-7
## 372 No Bad Under-7
## 373 No Bad Above-7
## 374 No Bad Above-7
## 375 No Bad Above-7
## 376 No Bad Above-7
## 377 Yes Bad Above-7
## 378 Yes Bad Above-7
## 379 Yes Bad Above-7
## 380 Yes Bad Above-7
## 381 No Bad Above-7
## 382 No Bad Above-7
## 383 Yes Good Under-7
## 384 Yes Good Under-7
## 385 Yes Good Under-7
## 386 Yes Good Under-7
## 387 Yes Good Under-7
## 388 Yes Good Under-7
## 389 No Good Above-7
## 390 No Good Above-7
## 391 Yes Good Above-7
## 392 Yes Good Above-7
## 393 Yes Good Under-7
## 394 Yes Good Under-7
## 395 Yes Good Under-7
## 396 Yes Good Under-7
## 397 Yes Bad Under-7
## 398 Yes Bad Under-7
## 399 Yes Good Under-7
## 400 Yes Good Under-7
## 401 No Good Above-7
## 402 No Good Above-7
## 403 No Good Under-7
## 404 No Good Under-7
## 405 Yes Good Under-7
## 406 Yes Good Under-7
## 407 No Bad Above-7
## 408 No Bad Above-7
## 409 No Bad Under-7
## 410 No Bad Under-7
## 411 No Good Under-7
## 412 No Good Under-7
## 413 Yes Good Under-7
## 414 Yes Good Under-7
## 415 No Good Above-7
## 416 No Good Above-7
## 417 Yes Good Under-7
## 418 Yes Good Under-7
## 419 Yes Good Under-7
## 420 Yes Good Under-7
## 421 Yes Good Under-7
## 422 Yes Good Under-7
## 423 Yes Good Above-7
## 424 Yes Good Above-7
## 425 Yes Good Under-7
## 426 Yes Good Under-7
## 427 Yes Good Above-7
## 428 Yes Good Above-7
## 429 No Bad Above-7
## 430 No Bad Above-7
## 431 No Bad Under-7
## 432 No Bad Under-7
## 433 No Bad Under-7
## 434 No Bad Under-7
## 435 Yes Good Above-7
## 436 Yes Good Above-7
## 437 Yes Good Under-7
## 438 Yes Good Under-7
## 439 Yes Good Under-7
## 440 Yes Good Under-7
## 441 Yes Good Under-7
## 442 Yes Good Under-7
## 443 Yes Good Above-7
## 444 Yes Good Above-7
## 445 Yes Good Above-7
## 446 Yes Good Above-7
## 447 Yes Good Under-7
## 448 Yes Good Under-7
## 449 Yes Good Under-7
## 450 Yes Good Under-7
## 451 Yes Good Under-7
## 452 Yes Good Under-7
## 453 Yes Good Above-7
## 454 Yes Good Above-7
## 455 Yes Good Under-7
## 456 Yes Good Under-7
## 457 Yes Good Under-7
## 458 Yes Good Under-7
## 459 Yes Good Under-7
## 460 Yes Good Under-7
## 461 Yes Good Above-7
## 462 Yes Good Above-7
## 463 Yes Good Above-7
## 464 Yes Good Above-7
## 465 Yes Good Under-7
## 466 Yes Good Under-7
## 467 Yes Good Under-7
## 468 Yes Good Under-7
## 469 No Bad Above-7
## 470 No Bad Above-7
## 471 Yes Good Under-7
## 472 Yes Good Under-7
## 473 Yes Good Under-7
## 474 Yes Good Under-7
## 475 No Bad Above-7
## 476 No Bad Above-7
## 477 No Bad Under-7
## 478 No Bad Under-7
## 479 No Bad Above-7
## 480 No Bad Above-7
## Class
## 1 M
## 2 M
## 3 L
## 4 L
## 5 M
## 6 M
## 7 L
## 8 M
## 9 M
## 10 M
## 11 H
## 12 M
## 13 L
## 14 L
## 15 H
## 16 M
## 17 M
## 18 M
## 19 M
## 20 H
## 21 M
## 22 M
## 23 M
## 24 L
## 25 L
## 26 L
## 27 M
## 28 L
## 29 M
## 30 M
## 31 H
## 32 L
## 33 L
## 34 L
## 35 L
## 36 L
## 37 L
## 38 M
## 39 L
## 40 M
## 41 L
## 42 M
## 43 L
## 44 M
## 45 M
## 46 L
## 47 L
## 48 H
## 49 H
## 50 M
## 51 L
## 52 L
## 53 M
## 54 H
## 55 L
## 56 L
## 57 L
## 58 L
## 59 M
## 60 M
## 61 L
## 62 M
## 63 H
## 64 M
## 65 L
## 66 L
## 67 M
## 68 H
## 69 H
## 70 M
## 71 L
## 72 M
## 73 L
## 74 M
## 75 L
## 76 M
## 77 M
## 78 M
## 79 L
## 80 H
## 81 L
## 82 L
## 83 L
## 84 M
## 85 H
## 86 L
## 87 H
## 88 L
## 89 L
## 90 L
## 91 L
## 92 H
## 93 H
## 94 H
## 95 L
## 96 H
## 97 H
## 98 M
## 99 M
## 100 M
## 101 M
## 102 H
## 103 L
## 104 L
## 105 M
## 106 L
## 107 M
## 108 H
## 109 M
## 110 M
## 111 H
## 112 M
## 113 L
## 114 L
## 115 L
## 116 L
## 117 M
## 118 H
## 119 M
## 120 M
## 121 M
## 122 L
## 123 M
## 124 M
## 125 L
## 126 L
## 127 M
## 128 L
## 129 L
## 130 L
## 131 L
## 132 M
## 133 L
## 134 L
## 135 H
## 136 H
## 137 H
## 138 M
## 139 H
## 140 M
## 141 L
## 142 L
## 143 M
## 144 H
## 145 L
## 146 M
## 147 H
## 148 M
## 149 M
## 150 H
## 151 H
## 152 M
## 153 H
## 154 L
## 155 M
## 156 H
## 157 M
## 158 M
## 159 L
## 160 M
## 161 H
## 162 M
## 163 H
## 164 M
## 165 M
## 166 H
## 167 M
## 168 H
## 169 H
## 170 M
## 171 H
## 172 M
## 173 L
## 174 L
## 175 M
## 176 L
## 177 H
## 178 M
## 179 H
## 180 M
## 181 H
## 182 L
## 183 H
## 184 M
## 185 L
## 186 H
## 187 M
## 188 M
## 189 H
## 190 M
## 191 L
## 192 L
## 193 M
## 194 M
## 195 M
## 196 M
## 197 H
## 198 H
## 199 L
## 200 M
## 201 H
## 202 H
## 203 M
## 204 M
## 205 L
## 206 H
## 207 M
## 208 M
## 209 M
## 210 M
## 211 H
## 212 M
## 213 H
## 214 L
## 215 L
## 216 L
## 217 M
## 218 M
## 219 H
## 220 M
## 221 M
## 222 M
## 223 M
## 224 H
## 225 H
## 226 M
## 227 L
## 228 L
## 229 H
## 230 L
## 231 M
## 232 L
## 233 M
## 234 M
## 235 M
## 236 L
## 237 L
## 238 M
## 239 M
## 240 H
## 241 H
## 242 M
## 243 L
## 244 M
## 245 H
## 246 M
## 247 H
## 248 M
## 249 L
## 250 M
## 251 H
## 252 L
## 253 M
## 254 L
## 255 H
## 256 H
## 257 H
## 258 M
## 259 M
## 260 L
## 261 L
## 262 M
## 263 M
## 264 M
## 265 M
## 266 H
## 267 M
## 268 M
## 269 M
## 270 M
## 271 H
## 272 M
## 273 M
## 274 H
## 275 H
## 276 H
## 277 H
## 278 H
## 279 M
## 280 M
## 281 H
## 282 H
## 283 H
## 284 H
## 285 M
## 286 M
## 287 H
## 288 H
## 289 M
## 290 M
## 291 L
## 292 L
## 293 H
## 294 H
## 295 M
## 296 M
## 297 H
## 298 H
## 299 M
## 300 M
## 301 L
## 302 L
## 303 M
## 304 M
## 305 M
## 306 M
## 307 H
## 308 H
## 309 M
## 310 M
## 311 M
## 312 M
## 313 H
## 314 H
## 315 H
## 316 H
## 317 H
## 318 H
## 319 H
## 320 H
## 321 M
## 322 M
## 323 L
## 324 L
## 325 M
## 326 M
## 327 L
## 328 L
## 329 M
## 330 M
## 331 L
## 332 L
## 333 M
## 334 M
## 335 L
## 336 L
## 337 M
## 338 M
## 339 M
## 340 M
## 341 M
## 342 M
## 343 M
## 344 M
## 345 H
## 346 H
## 347 H
## 348 H
## 349 L
## 350 L
## 351 L
## 352 L
## 353 M
## 354 M
## 355 H
## 356 H
## 357 M
## 358 M
## 359 H
## 360 H
## 361 M
## 362 M
## 363 H
## 364 H
## 365 H
## 366 H
## 367 M
## 368 M
## 369 H
## 370 H
## 371 M
## 372 M
## 373 L
## 374 L
## 375 L
## 376 L
## 377 M
## 378 M
## 379 L
## 380 L
## 381 L
## 382 L
## 383 H
## 384 H
## 385 H
## 386 H
## 387 M
## 388 M
## 389 L
## 390 L
## 391 M
## 392 M
## 393 H
## 394 H
## 395 H
## 396 H
## 397 M
## 398 M
## 399 H
## 400 H
## 401 M
## 402 M
## 403 H
## 404 H
## 405 H
## 406 H
## 407 L
## 408 L
## 409 M
## 410 M
## 411 H
## 412 H
## 413 M
## 414 M
## 415 L
## 416 L
## 417 H
## 418 H
## 419 H
## 420 H
## 421 H
## 422 H
## 423 M
## 424 M
## 425 H
## 426 H
## 427 M
## 428 M
## 429 L
## 430 L
## 431 H
## 432 H
## 433 M
## 434 M
## 435 M
## 436 M
## 437 M
## 438 M
## 439 M
## 440 M
## 441 M
## 442 M
## 443 M
## 444 M
## 445 L
## 446 L
## 447 M
## 448 M
## 449 H
## 450 H
## 451 H
## 452 H
## 453 M
## 454 M
## 455 M
## 456 M
## 457 H
## 458 H
## 459 H
## 460 H
## 461 M
## 462 M
## 463 M
## 464 M
## 465 H
## 466 H
## 467 H
## 468 H
## 469 L
## 470 L
## 471 M
## 472 M
## 473 M
## 474 M
## 475 L
## 476 L
## 477 M
## 478 M
## 479 L
## 480 LWe have 14 features left after removing 3 useless features.
Prediction model 1 – Decision tree
# Decision trees use probability to choose how to split, so here we "seed" a random number generator so our results are always the same.
set.seed(55)
# Split the data into 75% training, 25% test: this means we use 75% of the dataset to build our predictive model. We then use the remaining 25% of data as the unseen data (test) in order to evaluate the predictions.
ind <- sample(2, nrow(E1), replace=TRUE, prob=c(0.75, 0.25))
trainingData <- E1[ind==1,]
testData <- E1[ind==2,]#Use the training data to train a model.
formula <- Class ~ gender + NationalITy + PlaceofBirth + StageID + Topic + Relation + raisedhands + VisITedResources + AnnouncementsView + Discussion + ParentAnsweringSurvey + ParentschoolSatisfaction + StudentAbsenceDays
E_ctree <- ctree(formula, data = trainingData)plot(E_ctree)
tree.predict <- predict(E_ctree, newdata = testData)
confusionMatrix(tree.predict, testData$Class)## Confusion Matrix and Statistics
##
## Reference
## Prediction H M L
## H 24 7 0
## M 11 40 13
## L 0 4 21
##
## Overall Statistics
##
## Accuracy : 0.7083
## 95% CI : (0.6184, 0.7877)
## No Information Rate : 0.425
## P-Value [Acc > NIR] : 3.275e-10
##
## Kappa : 0.5435
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: H Class: M Class: L
## Sensitivity 0.6857 0.7843 0.6176
## Specificity 0.9176 0.6522 0.9535
## Pos Pred Value 0.7742 0.6250 0.8400
## Neg Pred Value 0.8764 0.8036 0.8632
## Prevalence 0.2917 0.4250 0.2833
## Detection Rate 0.2000 0.3333 0.1750
## Detection Prevalence 0.2583 0.5333 0.2083
## Balanced Accuracy 0.8017 0.7182 0.7856Heatmap
E_table <- table(tree.predict, testData$Class)
E_melt <- melt(E_table)
names(E_melt) <- c("Prediction", "Reference", "Value")
E_melt$Value <- (E_melt$Value - min(E_melt$Value))/(max(E_melt$Value) - min(E_melt$Value))
E_melt## Prediction Reference Value
## 1 H H 0.600
## 2 M H 0.275
## 3 L H 0.000
## 4 H M 0.175
## 5 M M 1.000
## 6 L M 0.100
## 7 H L 0.000
## 8 M L 0.325
## 9 L L 0.525ggplot(E_melt, aes(x=Reference, y=Prediction, fill=Value)) + geom_tile() + scale_fill_gradient(low = "white",high = "red") + scale_x_discrete(position = "top") + scale_y_discrete(limits = rev(levels(E_melt$Prediction)))
Summary of model 1: we get some findings from the tree mode above:
1. From the plot of the tree, some variables ( VisITedResources, StudentAbsenceDays, Relation, gender, ParentAnsweringSurvey and raisedhands) are selected as splitting rules, which means these features are crucial in splitting the tree. so they are very important features in this tree model.
2. From the confusion matrix and heatmap, there is no prediction error between class L and H. However, the performance is not so good between class M and H, M and L. It means that this model has some problem in predicting the adjacent classes but not the distant classes.
3. The accuracy of this model is 0.7038 from the statistics result. The accuracy is a description of systematic errors, a measure of statistical bias. It reflects the percentage of the true positive cases out of all the cases. In this case, we can see that 24 of H class have been predicted correctly which means the numbers of the true positive of H class, and 7 of M classes have been predicted as H classes but actually not H, which indicates numbers of the false positive of H class. To calculate accuracy, the true positive cases are accumulated as 84 (24+40+21) including all the H, M and L classes, and the total cases are 120 (24+7+11+40+13+4+21) including all the TP, TN, FP and FN cases. The formula of accuracy is TP/TP+TN+FP+FT, so the accuracy is 84/120 = 0.7038 in this model.The accuracy reflects how the model performs in predicting the correct classes out of all the cases.
4. Precision is a better method for us to judge the performance of a model. Precision is a description of random errors, a measure of statistical variability. The formula of precision is TP/TP+FP, which can reflect the rate of true positive cases out of all the positive cases including false positive cases. In this case, there are 13 L classes that predicted as M classes, these 13 cases are the FP cases in predicting M classes, and there are also 7 M classes predicted as H classes, these 7 cases is the FP cases in predicting H classes. We can get a better insight of the model by referring to the precision of each class.
In this tree model, precision is: H(0.9176), M(0.6522), L(0.9535), and the mean precision is 0.8411.
Prediction model 2 – Random Forest
rf.model <- randomForest(formula, data = trainingData, importance = TRUE,
ntree = 2000, nodesize = 20)
rf.predict <- predict(rf.model, testData)
confusionMatrix(testData$Class, rf.predict)## Confusion Matrix and Statistics
##
## Reference
## Prediction H M L
## H 22 13 0
## M 5 42 4
## L 0 7 27
##
## Overall Statistics
##
## Accuracy : 0.7583
## 95% CI : (0.6717, 0.8318)
## No Information Rate : 0.5167
## P-Value [Acc > NIR] : 4.645e-08
##
## Kappa : 0.6233
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: H Class: M Class: L
## Sensitivity 0.8148 0.6774 0.8710
## Specificity 0.8602 0.8448 0.9213
## Pos Pred Value 0.6286 0.8235 0.7941
## Neg Pred Value 0.9412 0.7101 0.9535
## Prevalence 0.2250 0.5167 0.2583
## Detection Rate 0.1833 0.3500 0.2250
## Detection Prevalence 0.2917 0.4250 0.2833
## Balanced Accuracy 0.8375 0.7611 0.8962varImpPlot(rf.model)
Summary of model 2: we get some findings from the tree mode above:
1. From the plot of variable importance, some variables ( StudentAbsenceDays, VisITedResources, raisedhands, AnnouncementsView, ParentAnsweringSurvey and Relation) are more important, and the other variables are less important in this model such as StageID, PlaceofBirth and Topic.
2. From the confusion matrix, there is also no prediction error between class L and H in this random forest model. There are some improvements: all the number of correct prediction has been increased in all the classes (26, 42 and 26) than the previous model. And the FP of M classes have been reduced a lot. However, the FP cases of H and L classes have been slightly increased.
3. The accuracy of this model 2 is 0.7583, which is a little higher than model 1.
4. In this random forest model, precision is: H(0.8681), M(0.8333), L(0.9213), and the mean precision is 0.8742.
Overall, this model is better than the tree model both in accuracy and precision.
Prediction model 3 – Support Vector Machines
svm.model <- svm(formula, data = trainingData, kernel = "radial", cost = 10, gamma = 0.15)
svm.predict <- predict(svm.model, testData)
confusionMatrix(testData$Class, svm.predict)## Confusion Matrix and Statistics
##
## Reference
## Prediction H M L
## H 29 6 0
## M 7 41 3
## L 1 8 25
##
## Overall Statistics
##
## Accuracy : 0.7917
## 95% CI : (0.708, 0.8604)
## No Information Rate : 0.4583
## P-Value [Acc > NIR] : 7.632e-14
##
## Kappa : 0.6791
## Mcnemar's Test P-Value : 0.3408
##
## Statistics by Class:
##
## Class: H Class: M Class: L
## Sensitivity 0.7838 0.7455 0.8929
## Specificity 0.9277 0.8462 0.9022
## Pos Pred Value 0.8286 0.8039 0.7353
## Neg Pred Value 0.9059 0.7971 0.9651
## Prevalence 0.3083 0.4583 0.2333
## Detection Rate 0.2417 0.3417 0.2083
## Detection Prevalence 0.2917 0.4250 0.2833
## Balanced Accuracy 0.8557 0.7958 0.8975Summary of model 3: we get some findings from the tree mode above:
1. From the confusion matrix, different from the previous two models, there is a prediction error between class L and H, one H class is predicted as L class. There are some improvements: all the number of correct prediction has been increased in all the classes (29, 41 and 25), much better than the model 1, but similar to model 2. And the FP of H and M classes have been reduced, which is the best among these 3 models.
2. The accuracy of this model 3 is 0.7917, which is a little higher than model 1 and model 2.
3. In this random forest model, precision is: H(0.9277), M(0.8462), L(0.9022), and the mean precision is 0.8920.
Overall, this model is best among these 3 models both in accuracy and precision.
Task 4. Improvement of these prediction models using N-folder cross validation for the prediction models above
Using N-folder cross validation on prediction model 1 – Decision tree
# using 10-folder cross validation to train this model
TrainingParameters <- trainControl(method = "cv", number = 10)
# train model with randomforest networks
trModel <- train(formula,
data = trainingData,
method = "ctree",
trControl= TrainingParameters,
preProcess=c("scale","center"),
na.action = na.omit
)## Loading required package: party## Loading required package: mvtnorm## Loading required package: modeltools## Loading required package: stats4## Loading required package: strucchange## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich##
## Attaching package: 'party'## The following objects are masked from 'package:partykit':
##
## cforest, ctree, ctree_control, edge_simple, mob, mob_control,
## node_barplot, node_bivplot, node_boxplot, node_inner,
## node_surv, node_terminal## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuelatrPredictions <-predict(trModel, testData)
# Create confusion matrix
confusionMatrix(testData$Class, trPredictions)## Confusion Matrix and Statistics
##
## Reference
## Prediction H M L
## H 30 5 0
## M 8 39 4
## L 1 12 21
##
## Overall Statistics
##
## Accuracy : 0.75
## 95% CI : (0.6627, 0.8245)
## No Information Rate : 0.4667
## P-Value [Acc > NIR] : 2.484e-10
##
## Kappa : 0.6141
## Mcnemar's Test P-Value : 0.1276
##
## Statistics by Class:
##
## Class: H Class: M Class: L
## Sensitivity 0.7692 0.6964 0.8400
## Specificity 0.9383 0.8125 0.8632
## Pos Pred Value 0.8571 0.7647 0.6176
## Neg Pred Value 0.8941 0.7536 0.9535
## Prevalence 0.3250 0.4667 0.2083
## Detection Rate 0.2500 0.3250 0.1750
## Detection Prevalence 0.2917 0.4250 0.2833
## Balanced Accuracy 0.8538 0.7545 0.8516# estimate variable importance
importance <- varImp(trModel, scale=FALSE)
# summarize importance
print(importance)## ROC curve variable importance
##
## variables are sorted by maximum importance across the classes
## H M L
## VisITedResources 0.9644 0.8995 0.9644
## StudentAbsenceDays 0.9491 0.8052 0.9491
## raisedhands 0.9469 0.8452 0.9469
## AnnouncementsView 0.8939 0.7945 0.8939
## ParentAnsweringSurvey 0.7904 0.6947 0.7904
## Relation 0.7637 0.6770 0.7637
## ParentschoolSatisfaction 0.7431 0.6445 0.7431
## Discussion 0.7379 0.6402 0.7379
## gender 0.6850 0.5929 0.6850
## Topic 0.5890 0.5890 0.5719
## PlaceofBirth 0.5392 0.5606 0.5606
## StageID 0.5503 0.5470 0.5503
## NationalITy 0.5073 0.5092 0.5092# plot importance
plot(importance)
Summary of model 1: we get some findings from the tree mode above:
1. From the plot of variable importance, the most important variables are StudentAbsenceDays, VisITedResources, raisedhands and AnnouncementsView, the less important variables are ParentAnsweringSurvey, relation, ParentschoolSatisfaction, Discussion and gender, and the unimportant variables are Nationality, StageID, PlaceofBirth and Topic for every class. This conclusion further confirmed our findings in the previous steps.
2. From the confusion matrix, there is a prediction error between class L and H, one H class is predicted as L class. There are some improvements: The number of TP of H class with 30 is much better than the model 1 without cross validation, it’s also the highest till now. And the FP are much smaller than the model 1 without cross validation. However, there are 12 m class predicted as L class, which is much high than the model 1 without cross validation.
3. The accuracy of this model is 0.75, which is higher than the accuracy of model1 without cross validation(0.7083).
4. In this model, precision is: H(0.9383), M(0.8125), L(0.8632), and the mean precision is 0.8713, which is higher than that of model 1 without cross validation (0.8411).
Overall, this model is better than the model 1 without cross validation both in accuracy and precision, and similar to the performance of model 2 without cross validation
Using N-folder cross validation on prediction model 2 – Random Forest
# train model with randomforest networks
rfModel <- train(formula,
data = trainingData,
method = "rf",
trControl= TrainingParameters,
preProcess=c("scale","center"),
na.action = na.omit
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuelarfPredictions <-predict(rfModel, testData)
# Create confusion matrix
confusionMatrix(rfPredictions, testData$Class)## Confusion Matrix and Statistics
##
## Reference
## Prediction H M L
## H 25 5 0
## M 10 42 7
## L 0 4 27
##
## Overall Statistics
##
## Accuracy : 0.7833
## 95% CI : (0.6989, 0.8533)
## No Information Rate : 0.425
## P-Value [Acc > NIR] : 1.259e-15
##
## Kappa : 0.664
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: H Class: M Class: L
## Sensitivity 0.7143 0.8235 0.7941
## Specificity 0.9412 0.7536 0.9535
## Pos Pred Value 0.8333 0.7119 0.8710
## Neg Pred Value 0.8889 0.8525 0.9213
## Prevalence 0.2917 0.4250 0.2833
## Detection Rate 0.2083 0.3500 0.2250
## Detection Prevalence 0.2500 0.4917 0.2583
## Balanced Accuracy 0.8277 0.7886 0.8738# estimate variable importance
importance <- varImp(rfModel, scale=FALSE)
# summarize importance
print(importance)## rf variable importance
##
## only 20 most important variables shown (out of 48)
##
## Overall
## VisITedResources 67.853
## StudentAbsenceDaysUnder-7 35.216
## raisedhands 26.329
## AnnouncementsView 21.496
## Discussion 19.582
## RelationMum 9.009
## genderM 5.801
## ParentAnsweringSurveyYes 5.096
## NationalITySaudiArabia 3.347
## TopicEnglish 3.320
## ParentschoolSatisfactionGood 3.084
## TopicGeology 2.356
## PlaceofBirthJordan 2.102
## TopicChemistry 2.071
## StageIDMiddleSchool 2.015
## TopicMath 1.961
## NationalITyPalestine 1.819
## StageIDLowerlevel 1.673
## NationalITyKW 1.631
## PlaceofBirthKuwaIT 1.473Summary of model 2: we conclude some findings from the tree mode above:
1. From the plot of variable importance, the most important variables are VisITedResources, raisedhands, StudentAbsenceDays, AnnouncementsView and Discussion, the less important variables are RelationMum, ParentAnsweringSurvey, genderM and ParentschoolSatisfaction, and the unimportant variables are Topic, StageID,Nationality and PlaceofBirth for every class. This figures that represent the level of importance is much clear, which also further confirmed our findings in the previous steps.
2. From the confusion matrix, there is no prediction error between class L and H in this random forest model. There are some improvements: The number of correct prediciton has been increased a lot in M and L class (43 and 28) than the model 1. And there are only 4 m class predicted as L class, which has been improved a lot than model 1.
3. The accuracy of this model is 0.8083, which is the highest accuracy among all the models.
4. In this model, precision is: H(0.9529), M(0.7826), L(0.9535), and the mean precision is 0.8963, which is highest precision among all the models till now.
Overall, this model is much better than the model 1 with cross validation both in accuracy and precision.
Using N-folder cross validation on prediction model 3 – Support Vector Machines
# train model with SVM
SVMModel <- train(formula,
data = trainingData,
method = "svmRadial", cost = 10, gamma = 0.15,
trControl= TrainingParameters,
preProcess=c("scale","center"),
na.action = na.omit
)## Loading required package: kernlab## Warning: package 'kernlab' was built under R version 3.4.1##
## Attaching package: 'kernlab'## The following object is masked from 'package:modeltools':
##
## prior## The following object is masked from 'package:ggplot2':
##
## alpha## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19,
## uniqueCut = 10, : These variables have zero variances: NationalITyvenzuela,
## PlaceofBirthvenzuela## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.SVMPredictions <-predict(SVMModel, testData)
# Create confusion matrix
confusionMatrix(SVMPredictions, testData$Class)## Confusion Matrix and Statistics
##
## Reference
## Prediction H M L
## H 22 2 0
## M 13 45 9
## L 0 4 25
##
## Overall Statistics
##
## Accuracy : 0.7667
## 95% CI : (0.6807, 0.839)
## No Information Rate : 0.425
## P-Value [Acc > NIR] : 2.733e-14
##
## Kappa : 0.6331
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: H Class: M Class: L
## Sensitivity 0.6286 0.8824 0.7353
## Specificity 0.9765 0.6812 0.9535
## Pos Pred Value 0.9167 0.6716 0.8621
## Neg Pred Value 0.8646 0.8868 0.9011
## Prevalence 0.2917 0.4250 0.2833
## Detection Rate 0.1833 0.3750 0.2083
## Detection Prevalence 0.2000 0.5583 0.2417
## Balanced Accuracy 0.8025 0.7818 0.8444# estimate variable importance
importance <- varImp(SVMModel, scale=FALSE)
# summarize importance
print(importance)## ROC curve variable importance
##
## variables are sorted by maximum importance across the classes
## H M L
## VisITedResources 0.9644 0.8995 0.9644
## StudentAbsenceDays 0.9491 0.8052 0.9491
## raisedhands 0.9469 0.8452 0.9469
## AnnouncementsView 0.8939 0.7945 0.8939
## ParentAnsweringSurvey 0.7904 0.6947 0.7904
## Relation 0.7637 0.6770 0.7637
## ParentschoolSatisfaction 0.7431 0.6445 0.7431
## Discussion 0.7379 0.6402 0.7379
## gender 0.6850 0.5929 0.6850
## Topic 0.5890 0.5890 0.5719
## PlaceofBirth 0.5392 0.5606 0.5606
## StageID 0.5503 0.5470 0.5503
## NationalITy 0.5073 0.5092 0.5092# plot importance
plot(importance)